Rational Covariance Extension for Boundary Data and Positive Real Lemma with Positive Semidefinite Matrix Solution
|
|
- Charity Hart
- 5 years ago
- Views:
Transcription
1 Preprints of the 18th FAC World Congress Milano (taly) August 28 - September 2, 2011 Rational Covariance Extension for Boundary Data and Positive Real Lemma with Positive Semidefinite Matrix Solution Y Kuroiwa (Seira Hazuki) ( kuroiwayohei@gmailcom, seirahazuki@gmailcom) Abstract: We consider the rational covariance extension problem with boundary data in terms of the positive real lemma and block discrete-time Schwarz form The solution of the positive real function has zeros, poles and spectral zeros on the stability boundary We use the positive real lemma for the positivity characterization, the matrix P of independent variable is positive semidefinite Keywords: Rational covariance extension, positive real lemma, positive semidefinite solution, block discrete-time Schwarz form 1 NTRODUCTON For a given partial covariance sequence, we want to parameterize rational extensions of the covariance sequence This is the rational covariance extension problem A parameterization of the rational covariance extensions in Georgiou (1987); Lindquist (1997) is quite applicable to some problems in systems and control, however, it inherits infinite and nonlinear properties of the spectral factorization due to the complicated relation of the zeros of the spectral factor and the poles of the positive real function n this paper, we study the rational covariance extension problem with focusing on the boundary data case, ie, zeros, poles and spectral zeros of the positive real function are on the stability boundary Our approach is the positive real lemma, see, eg, Balakrishnan and Vandenberghe (2003); wasaki and Hara (2005); wasaki (2007) We prove that the independent variable of the positive semidefinite matrix in the positive real lemma is positive semidefinite We use a parameterization of the rational covariance extensions by the block discrete-time Schwarz form in Kuroiwa (2009) t is derived by the theory of the matrix orthogonal polynomials, see, eg, Lindquist (1974), and it is the generalization of the scalar case in Georgiou (1987); Kimura (1987) Notations Real numbers are represented by R, and complex numbers are represented by C Denote by R j k j k real matrices m m denotes m m identity matrix, and 0 j k denotes j k zero matrix They are simply represented by and 0 if their dimensions are clear in the context We use the notations A 0 and A > 0 to denote that a matrix A is positive semidefinite and a matrix A is positive definite Denote by A T the transpose of a matrix A and by A the conjugate transpose of a matrix A The square root of a symmetric and positive definite matrix A is given by A = A 1 2 A 1 2 Denote by D = {z C : z < 1} the unit disc The outside of the closed unit disk is denoted by D c = {z C : z > 1} The unit circle is denoted by T = {z C : z = 1} The state-space realization of a transfer function G(z) is denoted by We also use a notation to denote G(z) G(z) = C(z A) 1 B + D G(z) = A B C D 2 PRELMNARES 21 Rational Covariance Extension with Boundary Data Given a partial covariance sequence (R 0, R 1,, R n ), which is positive in the sense that the m(n+1) m(n+1) block Toeplitz matrix R 0 R 1 R n Γ n+1 := R T 1 (1) Rn T R 0 is positive definite, we want to find an m m positive real function f(z) such that the series expansion of f(z) begins with 1 2 R 0 + R 1 z 1 + R 2 z R n z n (2) The solvability condition of this covariance extension problem is given by the positive definiteness of the block Toeplitz matrix Γ n+1, defined by (1) We state the definition of the positive realness in Anderson and Vongpanitlerd (2006) Copyright by the nternational Federation of Automatic Control (FAC) 4226
2 Preprints of the 18th FAC World Congress Milano (taly) August 28 - September 2, 2011 Definition: An m m rational function f(z) is positive real if f(z) is analytic in D c and f(e iθ )+f(e iθ ) is positive semidefinite for θ 0, 2π) We consider the case that the positive real function has singularities on the stability boundary T Then, the poles of f(z) on T are simple and the associated residue matrix is symmetric and positive semidefinite For a given positive real function f(z), there exists a stable spectral factor W(z) such that f(e iθ ) + f(e iθ ) = W(e iθ )W(e iθ ) Thus, we can formulate the rational covariance extension problem in terms of W(z) Namely, we seek a stable spectral factor W(z) such that W(e iθ )W(e iθ ) = k= Ĉ k e ikθ Ĉ k = C k for k = 0, 1,,n We consider the stable spectral factor of an ARMA type, ie, it is given by W(z) = A(z) 1 Σ(z), (3) A(z) and Σ(z) are m m matrix polynomials of degree n A(z) = A 0 + A 1 z + + A n z n Σ(z) = Σ 0 + Σ 1 z + + Σ n z n Then, the positive real function f(z) is given by f(z) = A(z) 1 B(z) (4) for an m m matrix polynomial B(z) of degree n B(z) = B 0 + B 1 z + + B n z n The positive realness implies that the matrix pseudopolynomial, defined by D(e iθ ) := A(e iθ )B(e iθ ) + B(e iθ )A(e iθ ), (5) is positive semidefinite for θ 0, 2π) Then, it is clear that D(e iθ ) = Σ(e iθ )Σ(e iθ ) (6) holds The spectral zeros of the positive real function are given by the zeros of Σ(z) The spectral zero α D with a vector u C m is given by u T Σ(α) = 0 n this paper, we focus on the case that the positive real function has zeros, poles, and spectral zeros are on the stability boundary T We only consider the self conjugate case, ie, if e jα is the spectral zero, then, e jα T is also the spectral zero Let e jα T be a zero or a pole of a positive real function Then, it is also the spectral zero of the positive real function The converse is also true, ie, if a spectral zero is on T, then, it is also zero or pole of the positive real function 22 Solution via Matrix Orthogonal Polynomials A parameterization of the solutions to the rational covariance extension problem is given in terms of the matrix orthogonal polynomials in Kuroiwa (2009) We assume that Γ n+1 > 0 and R 0 = For the block Toeplitz matrix Γ n+1, consider the upper Cholesky factorization of the block Toeplitz matrix Γ n+1 Γ n+1 = U n+1 Σ n+1 U T n+1, (7) Σ n+1 := Q n Q n Q 0 (8) and Q 0 = since R 0 = We denote the inverse of U n+1 by U 1 n+1 = A n,1 A n,n 0 A n 1,n Similarly, let us consider the lower Cholesky factorization of the block Toeplitz matrix Γ n+1 Γ n+1 = V n+1 Λ n+1 V T n+1, (9) S S 1 0 Λ n+1 = (10) 0 0 S n and S 0 = We denote the inverse of V n+1 by 0 0 Vn+1 1 = B 1,1 0 (11) B n,n B n,n 1 The left matrix orthogonal polynomials of the first kind are given by A n (z) A n 1 (z) A n,1 A n,n 0 A n 1,n 1 = 0 0 z n z n 1 (12) and the right matrix orthogonal polynomials of the first kind are given by 4227
3 Preprints of the 18th FAC World Congress Milano (taly) August 28 - September 2, 2011 B 1 (z) B n (z) T B1,1 T z BT n,n = 0 B T n,n 1 (13) z n 0 0 Γ n+1 := 1 1 (Mn M n+1 T ) (14) R1 Rn = R 1 T, (15) R n T R n 1 T 2R 1 2R n 0 2R n 1 M n+1 := 0 0 Then, the left matrix orthogonal polynomials of the second kind are given by C n (z) C n 1 (z) C n,1 C n,n 0 C n 1,n 1 = 0 0 z n z n 1, (16) C n,1 C n,n 0 C n 1,n 1 = U n+1 1 M n+1 (17) 0 0 Similarly, the right matrix orthogonal polynomials of the second kind are given by D 1 (z) D n (z) T D1,1 T D T n,n z = 0 D T n,n 1, (18) z n 0 0 D1,1 T D T n,n 0 D T n,n 1 = M n+1 V n+1 T A parameterization of the solutions to the rational covariance extension problem is given in terms of the matrix orthogonal polynomials in Kuroiwa (2009) Lemma 1 M l (z) := A n (z) + α 1 A n 1 (z) + + α n N l (z) := C n (z) + α 1 C n 1 (z) + + α n, A k (z) and C k (z), k = 1,,n are the left matrix orthogonal polynomials of the first and second kinds, defined by (12) and (16) Then, a rational function, f(z) = 1 2 M l(z) 1 N l (z), (19) is a solution to the rational covariance extension problem if α k R m m, k = 1,,n, is chosen such that (19) is positive real Similarly, let us define M r (z) := B n (z) + B n 1 (z)α α n N r (z) := D n (z) + D n 1 (z)α α n, B k (z) and D k (z), k = 1,,n, are the right matrix orthogonal polynomials of the first and second kinds, defined by (13) and (18) Then, a rational function, f(z) = 1 2 N r(z)m r (z) 1, (20) is a solution to the rational covariance extension problem if α k R m m, k = 1,,n, is chosen such that (20) is positive real 23 Block Discrete-time Schwarz Form We give a brief review of the state-space realization of (20) by the block discrete-time Schwarz form in Kuroiwa (2009) Consider the Yule-Walker equation of Γ n+1 Γn ρ n un 0mn m ρ T =, n S n ρ n := R T n R T 1 u n := B n,n B n,1 T S n is given in (10) and B n,k, k = 1,, n, are given in (11) t gives T u n = Γ 1 n ρ n S n = ρ T n Γ 1 n ρ n (21) F n := Λ 1 2 n Vn T (Z n u n e T T n )Vn Λ T 2 n = Λ T 2 n Vn T (Z n + Γ 1 n ρ ne T T n )Vn Λ T 2 n (22) K n+1 := Q 1 2 n P n S T 2 n, Z n := e n := T (23) 4228
4 Preprints of the 18th FAC World Congress Milano (taly) August 28 - September 2, 2011 Q n is given in (8) V n and Λ n are given in (9) and ˆF n := Λ T 2 n F n Λ T 2 n, α n α := α 1 Theorem 2 The state-space realization of (20) is given by f(z) = 2 + et 1 ˆF n (z ˆF n + αe T n ) 1 e 1 (24) A characterization of α k R m m, k = 1,, n, for which (24) is strictly positive real, is given by a linear matrix inequality in Kuroiwa (2009) The choice of the free parameter, α k = 0, k = 1,,n, yields the so-called maximum entropy solution Hence, the set of α, for which (24) is strictly positive real, has an interior point, which implies that the corresponding LM in Kuroiwa (2009) is feasible due to the constraint qualification This is important since if the constraint qualification is satisfied, then, it is directly extended to the non-strict LM in Boyd et al (1994) However, in Kuroiwa (2009), we used the standard change of the variable technique to derive the LM, the invertibility of a matrix is required t seems that the invertibility is not generally satisfied for the positive real function with the boundary data, which might yield a spectral factor of a non-minimal realization To this end, we state the positive real lemma for the boundary data We state the positive real lemma with positive semidefinite solution to deal with the situation that the poles of the positive real function are on the stability boundary T Lemma 3 Let G(z) = C(z A) 1 B + D be a transfer function with a minimal state-space realization, which is stable but not asymptotically stable Then, G(z) is positive real if and only if there exists P 0 such that M(P) 0, (25) P APA T B APC M(P) := T B T CPA T D + D T CPC T (26) L Moreover, let be a matrix factorization of M(P), W ie, Then, (A, L) is not controllable T L L M(P) = (27) W W Proof Note that and G(e iθ ) + G(e iθ ) = C(e iθ A) 1 0 B B T D + D T (e iθ A T ) 1 C T (28) C(e iθ A) 1 P APA T APC T CPA T CPC T (e iθ A T ) 1 C T 0 (29) hold, which is derived by P APA T = (e iθ A)P(e iθ A T ) +AP(e iθ A T ) + (e iθ A)PA T (e iθ A) 1 {P APA T }(e iθ A T ) 1 = P + (e iθ A) 1 AP + PA T (e iθ A T ) 1 = C(e iθ A) 1 {P APA T }(e iθ A T ) 1 C T = CPC T + C(e iθ A) 1 APC T +CPA T (e iθ A T ) 1 C T The sum of them yields G(e iθ ) + G(e iθ ) = C(e iθ A) 1 M(P) (e iθ A T ) 1 C T (30) 24 Positive Real Lemma With Positive Semidefinite Solution t is well-known that G(e iθ ) + G(e iθ ) 0 if and only if there exists a stable spectral factor W(z) such that G(e iθ ) + G(e iθ ) = W(e iθ )W(e iθ ) holds The necessary and sufficient condition of the existence of W(z) is that M(P) is positive semidefinite The state-space realization of W(z) is given by A L W(z) = C W Since M(P) is positive semidefinite, (27) holds for some matrices L and W, which yields the Lyapunov equation P APA T = LL T (31) We shall see that the pair (A, L) is not controllable and eigenmodes on the stability boundary are in the uncontrollable subspace The solution to (31) is given by P = A k LL T A Tk, (32) k=0 which is positive semidefinite since (A, L) is not controllable 0 e Ω k := j(ω k π 2 ) mk e j(ω k π 2 ) mk 0, k = 1,, r, 4229
5 Preprints of the 18th FAC World Congress Milano (taly) August 28 - September 2, 2011 for which e jω k, k = 1,,r, are the eigenvalues of A on T By the change of coordinates, we can put A in the form Ā := T 1 AT = diag Ω 1,,Ω r, s, t, A as (33) by a similar transformation, A as is an asymptotically stable matrix We can see that P = T 1 PT T (34) P = diag Γ 1,,Γ r, P s, P t, P as Pk Q Γ k := k Q T k P k P k, P s, P t and P as are symmetric matrices and Q k are skew-symmetric matrices since holds in (26), see Boyd et al (1994) P APA T 0 (35) Then, the left hand side of (31) becomes P as A as P as A T as By considering (31), there exists L such that holds T 1 L = 0 L Denote by l the size of A Then, rank L AL A l 1 L = rank L A as L A l 1 L as < l Thus, (A, L) is not controllable 3 POSTVE REALNESS WTH BOUNDARY DATA The condition that the zeros of f(z) of (24) are on the stability boundary T is given by interpolation conditions on f(z), r T k f(z k ) = 0, k = 1,,l z, (36) z k, k = 1,,l z, are self conjugate points on T They are also the spectral zeros of f(z) on T We assume that l z is strictly less than mn, which is the McMillan degree of (24) Since f(z) is also given by (20), (36) implies r T k N r (z k )M r (z k ) 1 = 0 r T k N r(z k ) = 0, k = 1, l z if M r (z k ), k = 1, l z are invertible We obtain the linear equation Λ z = Ξ z α, r T 1 D n (z 1 ) Λ z := rl T z D n (z lz ) r1 T D 1 (z 1 ) r T 1 D n 1 (z 1 ) Ξ z := rl T D 1(z lz ) rl T z D n 1 (z lz ) Similarly, the condition that the poles of f(z) are on the stability boundary T is given by interpolation conditions on f(z) 1, r T k f(p k ) 1 = 0, k = 1,,l p, (37) q k, k = 1,,l p, are self conjugate points on T We assume that l p is strictly less than mn l z They are also the spectral zeros of f(z) on T n terms of (20), (37) implies r T k M r (p k )N r (p k ) 1 = 0 rk T M r (p k ) = 0, k = 1, l p if N r (p k ), k = 1, l p are invertible Thus, we obtain the linear equation Λ p = Ξ p α, r T 1 B n (p 1 ) Λ p := rl T p B n (p lp ) r1 T B 1 (p 1 ) r T 1 B n 1 (p 1 ) Ξ p := rl T p B 1 (p lp ) rl T p B n 1 (p lp ) The two linear equations of α are combined to Λ := Λz Λ = Ξα, Λ p, Ξ := Ξz Ξ p Note that (l z + l p ) mn matrix Ξ is flat Under the assumption that Ξ has the full row rank, the general solution for α is given by α = α 0 + Ξ β, (38) α 0 := Ξ (ΞΞ ) 1 Λ Ξ is an mn (mn l z l p ) matrix with full column rank such that ΞΞ = 0, and β is an (mn l z l p ) m matrix By substituting (38) into (24), we obtain 4230
6 Preprints of the 18th FAC World Congress Milano (taly) August 28 - September 2, 2011 ˆF n α 0 e n Ξ βe n e 1 f(z) = e ˆF (39) 1 n 2 We give a characterization of β such that (39) is positive real Theorem 4 The transfer function (39) is positive real if and only if there exists P 0 and β such that P e1 e 1 ˆFn α 0 e n Ξ βe n e ˆF 1 n P ( ˆF n α 0 e n Ξ βe n) ˆF n e 1 0 (40) Proof t is due to Lemma 3 4 DSCUSSON A convex relaxation of (40), which is non-convex with respect to P and β, is possible if the matrix P is invertible by using a similar scheme in Fu and Mahata (2005); Kuroiwa (2009, 2010) A dual result of Lemma 3 is also derived from (25) by using the Schur complement if P is invertible Clearly, we can derive the dual form by using a similar algebra in terms of the conjugate spectral factor of the positive real function The progress on the positive real lemma is reported in Kuroiwa (2011b,a) We state the positive real lemma to the boundary data for discrete-time systems The positive real lemma for continuous-time systems in Popov (1962); Yakubovich (1962); Kalman (1963) is also generalized for the boundary case similar to Lemma 3, which is in Kuroiwa (2011b) Thus, we can also study the interpolation problem with the boundary data of the continuous-time systems Our approach to the rational covariance extension problem with boundary data is numerically tractable However, it is not clear whether or not the theory in Georgiou (1987); Byrnes et al (1995) can deal with the case that the positive real function has poles on the stability boundary wasaki, T and Hara, S (2005) Generalized KYP lemma: Unified frequency domain inequalities with design applications EEE Trans Automat Control, 50(1), Kalman, RE (1963) Lyapunov functions for the problem of Lur e in automatic control Proc of the National Academy of Sciences, 49(2) Kimura, H (1987) Positive partial realization of covariance sequences n C Byrnes and A Lindquist (eds), Modeling, dentification and Robust Control, North-Holland, Amsterdam Kuroiwa, Y (2009) Block discrete-time Schwarz form of multivariable rational interpolation and positivity by linear matrix inequality n Proceedings of 17th European Signal Processing Conference Glasgow, Scotland Kuroiwa, Y (2010) LM conditions of strictly bounded realness on a state-space realization to bi-tangential rational interpolation n Proceedings of 19th nternational Symposium of Mathematical Theory of Networks and Systems Budapest, Hungary Kuroiwa, YSH (2011a) Positive real lemma of multidimensional systems with application to rational spectral factorization submitted Kuroiwa, YSH (2011b) Positive real lemma with positive semidefinite solution and a difference in continuoustime and discrete-time systems theories submitted Lindquist, A (1974) A new algorithm for optimal filtering of discrete-time stationary processes SAM J Control, 12(4), Lindquist, A (1997) Recent progress in the partial stochastic realization problem n U Helmke, D Pratzel-Wolters, and E Zerz (eds), Operators, Systems, and Linear Algebra Popov, VM (1962) Absolute stability of nonlinear systems of automatic control Automation and Remote Control, 22, Yakubovich, VA (1962) Solution of certain matrix inequalities encountered in non-linear regulation theory Doklady Akademii Nauk SSSR, 143, REFERENCES Anderson, BDO and Vongpanitlerd, S (2006) Network Analysis and Synthesis-A Modern Systems Approach Dover Balakrishnan, V and Vandenberghe, L (2003) Semidefinite programming duality and linear time-invariant systems EEE Trans Automat Control, 48(1), Boyd, S, Ghaoui, LE, Feron, E, and Balakrishnan, V (1994) Linear Matrix nequalities in Systems and Control Theory SAM, Philadelphia Byrnes, C, Lindquist, A, Gusev, SV, and Matveev, AS (1995) A complete parameterization of all positive rational extensions of a covariance sequence EEE Trans Automat Control, 40(11), Fu, M and Mahata, K (2005) On constrained covariance extension problems n Proceedings of the 44th EEE Conference on Decision and Control, and the European Control Conference, Seville, Spain Georgiou, TT (1987) Realization of power spectra from partial covariance sequences EEE Trans Acoustics, Speech and Signal Processing, 35(4), wasaki, T (2007) Multivariable Control Lecture Notes, University of Virginia 4231
On some interpolation problems
On some interpolation problems A. Gombani Gy. Michaletzky LADSEB-CNR Eötvös Loránd University Corso Stati Uniti 4 H-1111 Pázmány Péter sétány 1/C, 35127 Padova, Italy Computer and Automation Institute
More informationRank-one LMIs and Lyapunov's Inequality. Gjerrit Meinsma 4. Abstract. We describe a new proof of the well-known Lyapunov's matrix inequality about
Rank-one LMIs and Lyapunov's Inequality Didier Henrion 1;; Gjerrit Meinsma Abstract We describe a new proof of the well-known Lyapunov's matrix inequality about the location of the eigenvalues of a matrix
More informationRECURSIVE ESTIMATION AND KALMAN FILTERING
Chapter 3 RECURSIVE ESTIMATION AND KALMAN FILTERING 3. The Discrete Time Kalman Filter Consider the following estimation problem. Given the stochastic system with x k+ = Ax k + Gw k (3.) y k = Cx k + Hv
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 informationFast Algorithms for SDPs derived from the Kalman-Yakubovich-Popov Lemma
Fast Algorithms for SDPs derived from the Kalman-Yakubovich-Popov Lemma Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University 8 September 2003 European Union RTN Summer School on Multi-Agent
More informationLMI relaxations in robust control (tutorial)
LM relaxations in robust control tutorial CW Scherer Delft Center for Systems and Control Delft University of Technology Mekelweg 2, 2628 CD Delft, The Netherlands cwscherer@dcsctudelftnl Abstract This
More informationOn Positive Real Lemma for Non-minimal Realization Systems
Proceedings of the 17th World Congress The International Federation of Automatic Control On Positive Real Lemma for Non-minimal Realization Systems Sadaaki Kunimatsu Kim Sang-Hoon Takao Fujii Mitsuaki
More informationAdvanced Digital Signal Processing -Introduction
Advanced Digital Signal Processing -Introduction LECTURE-2 1 AP9211- ADVANCED DIGITAL SIGNAL PROCESSING UNIT I DISCRETE RANDOM SIGNAL PROCESSING Discrete Random Processes- Ensemble Averages, Stationary
More informationFrom Convex Optimization to Linear Matrix Inequalities
Dep. of Information Engineering University of Pisa (Italy) From Convex Optimization to Linear Matrix Inequalities eng. Sergio Grammatico grammatico.sergio@gmail.com Class of Identification of Uncertain
More informationAn Observation on the Positive Real Lemma
Journal of Mathematical Analysis and Applications 255, 48 49 (21) doi:1.16/jmaa.2.7241, available online at http://www.idealibrary.com on An Observation on the Positive Real Lemma Luciano Pandolfi Dipartimento
More informationA Method to Teach the Parameterization of All Stabilizing Controllers
Preprints of the 8th FAC World Congress Milano (taly) August 8 - September, A Method to Teach the Parameterization of All Stabilizing Controllers Vladimír Kučera* *Czech Technical University in Prague,
More informationTHIS paper studies the input design problem in system identification.
1534 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 50, NO. 10, OCTOBER 2005 Input Design Via LMIs Admitting Frequency-Wise Model Specifications in Confidence Regions Henrik Jansson Håkan Hjalmarsson, Member,
More informationDownloaded 03/18/13 to Redistribution subject to SIAM license or copyright; see
SIAM J. CONTROL OPTIM. Vol. 37, No., pp. 2 229 c 998 Society for Industrial and Applied Mathematics A CONVEX OPTIMIZATION APPROACH TO THE RATIONAL COVARIANCE EXTENSION PROBLEM CHRISTOPHER I. BYRNES, SERGEI
More informationLecture 1. 1 Conic programming. MA 796S: Convex Optimization and Interior Point Methods October 8, Consider the conic program. min.
MA 796S: Convex Optimization and Interior Point Methods October 8, 2007 Lecture 1 Lecturer: Kartik Sivaramakrishnan Scribe: Kartik Sivaramakrishnan 1 Conic programming Consider the conic program min s.t.
More informationH 2 -optimal model reduction of MIMO systems
H 2 -optimal model reduction of MIMO systems P. Van Dooren K. A. Gallivan P.-A. Absil Abstract We consider the problem of approximating a p m rational transfer function Hs of high degree by another p m
More informationA Parameterization of Positive Real Residue Interpolants with McMillan Degree Constraint
A Parameterization of Positive Real Residue Interpolants with McMillan Degree Constraint Yohei Kuroiwa Abstract A parameterization of the solutions to the positive real residue interpolation with McMillan
More informationINPUT-TO-STATE COVARIANCES FOR SPECTRAL ANALYSIS: THE BIASED ESTIMATE
INPUT-TO-STATE COVARIANCES FOR SPECTRAL ANALYSIS: THE BIASED ESTIMATE JOHAN KARLSSON AND PER ENQVIST Abstract. In many practical applications second order moments are used for estimation of power spectrum.
More informationIntroduction 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 informationJune Engineering Department, Stanford University. System Analysis and Synthesis. Linear Matrix Inequalities. Stephen Boyd (E.
Stephen Boyd (E. Feron :::) System Analysis and Synthesis Control Linear Matrix Inequalities via Engineering Department, Stanford University Electrical June 1993 ACC, 1 linear matrix inequalities (LMIs)
More informationCONVEX OPTIMIZATION OVER POSITIVE POLYNOMIALS AND FILTER DESIGN. Y. Genin, Y. Hachez, Yu. Nesterov, P. Van Dooren
CONVEX OPTIMIZATION OVER POSITIVE POLYNOMIALS AND FILTER DESIGN Y. Genin, Y. Hachez, Yu. Nesterov, P. Van Dooren CESAME, Université catholique de Louvain Bâtiment Euler, Avenue G. Lemaître 4-6 B-1348 Louvain-la-Neuve,
More informationResearch Article An Equivalent LMI Representation of Bounded Real Lemma for Continuous-Time Systems
Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 28, Article ID 67295, 8 pages doi:1.1155/28/67295 Research Article An Equivalent LMI Representation of Bounded Real Lemma
More informationOn Linear-Quadratic Control Theory of Implicit Difference Equations
On Linear-Quadratic Control Theory of Implicit Difference Equations Daniel Bankmann Technische Universität Berlin 10. Elgersburg Workshop 2016 February 9, 2016 D. Bankmann (TU Berlin) Control of IDEs February
More informationExperimental evidence showing that stochastic subspace identication methods may fail 1
Systems & Control Letters 34 (1998) 303 312 Experimental evidence showing that stochastic subspace identication methods may fail 1 Anders Dahlen, Anders Lindquist, Jorge Mari Division of Optimization and
More informationStatistical Signal Processing Detection, Estimation, and Time Series Analysis
Statistical Signal Processing Detection, Estimation, and Time Series Analysis Louis L. Scharf University of Colorado at Boulder with Cedric Demeure collaborating on Chapters 10 and 11 A TT ADDISON-WESLEY
More informationBalanced 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 informationMulti-Model Adaptive Regulation for a Family of Systems Containing Different Zero Structures
Preprints of the 19th World Congress The International Federation of Automatic Control Multi-Model Adaptive Regulation for a Family of Systems Containing Different Zero Structures Eric Peterson Harry G.
More informationOn the Kalman-Yacubovich-Popov lemma and common Lyapunov solutions for matrices with regular inertia
On the Kalman-Yacubovich-Popov lemma and common Lyapunov solutions for matrices with regular inertia Oliver Mason, Robert Shorten and Selim Solmaz Abstract In this paper we extend the classical Lefschetz
More informationLecture Note 5: Semidefinite Programming for Stability Analysis
ECE7850: Hybrid Systems:Theory and Applications Lecture Note 5: Semidefinite Programming for Stability Analysis Wei Zhang Assistant Professor Department of Electrical and Computer Engineering Ohio State
More informationPRESENT day signal processing is firmly rooted in the
212 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 52, NO 2, FEBRUARY 2007 The Carathéodory Fejér Pisarenko Decomposition Its Multivariable Counterpart Tryphon T Georgiou, Fellow, IEEE Abstract When a covariance
More informationROBUST ANALYSIS WITH LINEAR MATRIX INEQUALITIES AND POLYNOMIAL MATRICES. Didier HENRION henrion
GRADUATE COURSE ON POLYNOMIAL METHODS FOR ROBUST CONTROL PART IV.1 ROBUST ANALYSIS WITH LINEAR MATRIX INEQUALITIES AND POLYNOMIAL MATRICES Didier HENRION www.laas.fr/ henrion henrion@laas.fr Airbus assembly
More informationDissipative Systems Analysis and Control
Bernard Brogliato, Rogelio Lozano, Bernhard Maschke and Olav Egeland Dissipative Systems Analysis and Control Theory and Applications 2nd Edition With 94 Figures 4y Sprin er 1 Introduction 1 1.1 Example
More informationStability of Parameter Adaptation Algorithms. Big picture
ME5895, UConn, Fall 215 Prof. Xu Chen Big picture For ˆθ (k + 1) = ˆθ (k) + [correction term] we haven t talked about whether ˆθ(k) will converge to the true value θ if k. We haven t even talked about
More informationMATH 722, COMPLEX ANALYSIS, SPRING 2009 PART 5
MATH 722, COMPLEX ANALYSIS, SPRING 2009 PART 5.. The Arzela-Ascoli Theorem.. The Riemann mapping theorem Let X be a metric space, and let F be a family of continuous complex-valued functions on X. We have
More information1. Let m 1 and n 1 be two natural numbers such that m > n. Which of the following is/are true?
. Let m and n be two natural numbers such that m > n. Which of the following is/are true? (i) A linear system of m equations in n variables is always consistent. (ii) A linear system of n equations in
More informationStatic Output Feedback Stabilisation with H Performance for a Class of Plants
Static Output Feedback Stabilisation with H Performance for a Class of Plants E. Prempain and I. Postlethwaite Control and Instrumentation Research, Department of Engineering, University of Leicester,
More informationEE/ACM Applications of Convex Optimization in Signal Processing and Communications Lecture 2
EE/ACM 150 - Applications of Convex Optimization in Signal Processing and Communications Lecture 2 Andre Tkacenko Signal Processing Research Group Jet Propulsion Laboratory April 5, 2012 Andre Tkacenko
More information9. Numerical linear algebra background
Convex Optimization Boyd & Vandenberghe 9. Numerical linear algebra background matrix structure and algorithm complexity solving linear equations with factored matrices LU, Cholesky, LDL T factorization
More informationINTEGRATION WORKSHOP 2003 COMPLEX ANALYSIS EXERCISES
INTEGRATION WORKSHOP 23 COMPLEX ANALYSIS EXERCISES DOUGLAS ULMER 1. Meromorphic functions on the Riemann sphere It s often useful to allow functions to take the value. This exercise outlines one way to
More informationHomework 2 Foundations of Computational Math 2 Spring 2019
Homework 2 Foundations of Computational Math 2 Spring 2019 Problem 2.1 (2.1.a) Suppose (v 1,λ 1 )and(v 2,λ 2 ) are eigenpairs for a matrix A C n n. Show that if λ 1 λ 2 then v 1 and v 2 are linearly independent.
More informationDiscrete-Time H Gaussian Filter
Proceedings of the 17th World Congress The International Federation of Automatic Control Discrete-Time H Gaussian Filter Ali Tahmasebi and Xiang Chen Department of Electrical and Computer Engineering,
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 informationConvex Optimization Approach to Dynamic Output Feedback Control for Delay Differential Systems of Neutral Type 1,2
journal of optimization theory and applications: Vol. 127 No. 2 pp. 411 423 November 2005 ( 2005) DOI: 10.1007/s10957-005-6552-7 Convex Optimization Approach to Dynamic Output Feedback Control for Delay
More informationA Maximum Entropy Enhancement for a Family of High-Resolution Spectral Estimators Augusto Ferrante, Michele Pavon, and Mattia Zorzi
318 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 57, NO. 2, FEBRUARY 2012 A Maximum Entropy Enhancement for a Family of High-Resolution Spectral Estimators Augusto Ferrante, Michele Pavon, and Mattia Zorzi
More informationMathematical Optimisation, Chpt 2: Linear Equations and inequalities
Mathematical Optimisation, Chpt 2: Linear Equations and inequalities Peter J.C. Dickinson p.j.c.dickinson@utwente.nl http://dickinson.website version: 12/02/18 Monday 5th February 2018 Peter J.C. Dickinson
More informationADAPTIVE FILTER THEORY
ADAPTIVE FILTER THEORY Fourth Edition Simon Haykin Communications Research Laboratory McMaster University Hamilton, Ontario, Canada Front ice Hall PRENTICE HALL Upper Saddle River, New Jersey 07458 Preface
More informationA Convex Optimization Approach to Generalized Moment Problems
This is page 1 Printer: Opaque this A Convex Optimization Approach to Generalized Moment Problems Christopher. Byrnes and Anders Lindquist ABSTRACT n this paper we present a universal solution to the generalized
More information9. Numerical linear algebra background
Convex Optimization Boyd & Vandenberghe 9. Numerical linear algebra background matrix structure and algorithm complexity solving linear equations with factored matrices LU, Cholesky, LDL T factorization
More informationA semidefinite relaxation scheme for quadratically constrained quadratic problems with an additional linear constraint
Iranian Journal of Operations Research Vol. 2, No. 2, 20, pp. 29-34 A semidefinite relaxation scheme for quadratically constrained quadratic problems with an additional linear constraint M. Salahi Semidefinite
More informationNonlinear 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 informationRobust exact pole placement via an LMI-based algorithm
Proceedings of the 44th EEE Conference on Decision and Control, and the European Control Conference 25 Seville, Spain, December 12-15, 25 ThC5.2 Robust exact pole placement via an LM-based algorithm M.
More informationProperties of Zero-Free Spectral Matrices Brian D. O. Anderson, Life Fellow, IEEE, and Manfred Deistler, Fellow, IEEE
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 54, NO 10, OCTOBER 2009 2365 Properties of Zero-Free Spectral Matrices Brian D O Anderson, Life Fellow, IEEE, and Manfred Deistler, Fellow, IEEE Abstract In
More informationSTABILITY-PRESERVING model reduction is a topic
1 Stability-Preserving Rational Approximation Subject to Interpolation Constraints Johan Karlsson and Anders Lindquist Abstract A quite comprehensive theory of analytic interpolation with degree constraint,
More informationTime Series Examples Sheet
Lent Term 2001 Richard Weber Time Series Examples Sheet This is the examples sheet for the M. Phil. course in Time Series. A copy can be found at: http://www.statslab.cam.ac.uk/~rrw1/timeseries/ Throughout,
More informationMatrix Mathematics. Theory, Facts, and Formulas with Application to Linear Systems Theory. Dennis S. Bernstein
Matrix Mathematics Theory, Facts, and Formulas with Application to Linear Systems Theory Dennis S. Bernstein PRINCETON UNIVERSITY PRESS PRINCETON AND OXFORD Contents Special Symbols xv Conventions, Notation,
More informationSchur parametrizations and balanced realizations of real discrete-time stable all-pass systems.
1 Schur parametrizations and balanced realizations of real discrete-time stable all-pass systems Martine Olivi Jean-Paul Marmorat Bernard Hanzon Ralf LM Peeters Abstract We investigate the parametrization
More informationState estimation of uncertain multiple model with unknown inputs
State estimation of uncertain multiple model with unknown inputs Abdelkader Akhenak, Mohammed Chadli, Didier Maquin and José Ragot Centre de Recherche en Automatique de Nancy, CNRS UMR 79 Institut National
More informationJoão P. Hespanha. January 16, 2009
LINEAR SYSTEMS THEORY João P. Hespanha January 16, 2009 Disclaimer: This is a draft and probably contains a few typos. Comments and information about typos are welcome. Please contact the author at hespanha@ece.ucsb.edu.
More informationOptimization Based Output Feedback Control Design in Descriptor Systems
Trabalho apresentado no XXXVII CNMAC, S.J. dos Campos - SP, 017. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics Optimization Based Output Feedback Control Design in
More informationChance Constrained Input Design
5th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC) Orlando, FL, USA, December -5, Chance Constrained Input Design Cristian R. Rojas, Dimitrios Katselis, Håkan Hjalmarsson,
More informationSDP APPROXIMATION OF THE HALF DELAY AND THE DESIGN OF HILBERT PAIRS. Bogdan Dumitrescu
SDP APPROXIMATION OF THE HALF DELAY AND THE DESIGN OF HILBERT PAIRS Bogdan Dumitrescu Tampere International Center for Signal Processing Tampere University of Technology P.O.Box 553, 3311 Tampere, FINLAND
More informationSelected Examples of CONIC DUALITY AT WORK Robust Linear Optimization Synthesis of Linear Controllers Matrix Cube Theorem A.
. Selected Examples of CONIC DUALITY AT WORK Robust Linear Optimization Synthesis of Linear Controllers Matrix Cube Theorem A. Nemirovski Arkadi.Nemirovski@isye.gatech.edu Linear Optimization Problem,
More informationA functional model for commuting pairs of contractions and the symmetrized bidisc
A functional model for commuting pairs of contractions and the symmetrized bidisc Nicholas Young Leeds and Newcastle Universities Lecture 2 The symmetrized bidisc Γ and Γ-contractions St Petersburg, June
More informationSubdiagonal pivot structures and associated canonical forms under state isometries
Preprints of the 15th IFAC Symposium on System Identification Saint-Malo, France, July 6-8, 29 Subdiagonal pivot structures and associated canonical forms under state isometries Bernard Hanzon Martine
More informationBOUNDED REAL LEMMA FOR MULTIVARIATE TRIGONOMETRIC MATRIX POLYNOMIALS AND FIR FILTER DESIGN APPLICATIONS
7th European Signal Processing Conference (EUSIPCO 009) Glasgow, Scotland, August 4-8, 009 BOUNDED REAL LEMMA FOR MULTIVARIATE TRIGONOMETRIC MATRIX POLYNOMIALS AND FIR FILTER DESIGN APPLICATIONS Bogdan
More informationContents. 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 informationMULTIDIMENSIONAL SCHUR COEFFICIENTS AND BIBO STABILITY
COMMUNICATIONS IN INFORMATION AND SYSTEMS c 2005 International Press Vol. 5, No. 1, pp. 131-142, 2005 006 MULTIDIMENSIONAL SCHUR COEFFICIENTS AND BIBO STABILITY I. SERBAN, F. TURCU, Y. STITOU, AND M. NAJIM
More information5.3 The Upper Half Plane
Remark. Combining Schwarz Lemma with the map g α, we can obtain some inequalities of analytic maps f : D D. For example, if z D and w = f(z) D, then the composition h := g w f g z satisfies the condition
More informationMIT Algebraic techniques and semidefinite optimization May 9, Lecture 21. Lecturer: Pablo A. Parrilo Scribe:???
MIT 6.972 Algebraic techniques and semidefinite optimization May 9, 2006 Lecture 2 Lecturer: Pablo A. Parrilo Scribe:??? In this lecture we study techniques to exploit the symmetry that can be present
More informationCANONICAL LOSSLESS STATE-SPACE SYSTEMS: STAIRCASE FORMS AND THE SCHUR ALGORITHM
CANONICAL LOSSLESS STATE-SPACE SYSTEMS: STAIRCASE FORMS AND THE SCHUR ALGORITHM Ralf L.M. Peeters Bernard Hanzon Martine Olivi Dept. Mathematics, Universiteit Maastricht, P.O. Box 616, 6200 MD Maastricht,
More informationParameterized Linear Matrix Inequality Techniques in Fuzzy Control System Design
324 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 2, APRIL 2001 Parameterized Linear Matrix Inequality Techniques in Fuzzy Control System Design H. D. Tuan, P. Apkarian, T. Narikiyo, and Y. Yamamoto
More informationSTUDY PLAN MASTER IN (MATHEMATICS) (Thesis Track)
STUDY PLAN MASTER IN (MATHEMATICS) (Thesis Track) I. GENERAL RULES AND CONDITIONS: 1- This plan conforms to the regulations of the general frame of the Master programs. 2- Areas of specialty of admission
More informationRobust and Optimal Control, Spring 2015
Robust and Optimal Control, Spring 2015 Instructor: Prof. Masayuki Fujita (S5-303B) D. Linear Matrix Inequality D.1 Convex Optimization D.2 Linear Matrix Inequality(LMI) D.3 Control Design and LMI Formulation
More informationADAPTIVE FILTER THEORY
ADAPTIVE FILTER THEORY Fifth Edition Simon Haykin Communications Research Laboratory McMaster University Hamilton, Ontario, Canada International Edition contributions by Telagarapu Prabhakar Department
More informationProjection of state space realizations
Chapter 1 Projection of state space realizations Antoine Vandendorpe and Paul Van Dooren Department of Mathematical Engineering Université catholique de Louvain B-1348 Louvain-la-Neuve Belgium 1.0.1 Description
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 informationProf. Dr.-Ing. Armin Dekorsy Department of Communications Engineering. Stochastic Processes and Linear Algebra Recap Slides
Prof. Dr.-Ing. Armin Dekorsy Department of Communications Engineering Stochastic Processes and Linear Algebra Recap Slides Stochastic processes and variables XX tt 0 = XX xx nn (tt) xx 2 (tt) XX tt XX
More informationELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications
ELE539A: Optimization of Communication Systems Lecture 15: Semidefinite Programming, Detection and Estimation Applications Professor M. Chiang Electrical Engineering Department, Princeton University March
More informationStatistical and Adaptive Signal Processing
r Statistical and Adaptive Signal Processing Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing Dimitris G. Manolakis Massachusetts Institute of Technology Lincoln Laboratory
More informationORF 523 Lecture 9 Spring 2016, Princeton University Instructor: A.A. Ahmadi Scribe: G. Hall Thursday, March 10, 2016
ORF 523 Lecture 9 Spring 2016, Princeton University Instructor: A.A. Ahmadi Scribe: G. Hall Thursday, March 10, 2016 When in doubt on the accuracy of these notes, please cross check with the instructor
More informationElementary linear algebra
Chapter 1 Elementary linear algebra 1.1 Vector spaces Vector spaces owe their importance to the fact that so many models arising in the solutions of specific problems turn out to be vector spaces. The
More informationA Simple Derivation of Right Interactor for Tall Transfer Function Matrices and its Application to Inner-Outer Factorization Continuous-Time Case
A Simple Derivation of Right Interactor for Tall Transfer Function Matrices and its Application to Inner-Outer Factorization Continuous-Time Case ATARU KASE Osaka Institute of Technology Department of
More informationAn LQ R weight selection approach to the discrete generalized H 2 control problem
INT. J. CONTROL, 1998, VOL. 71, NO. 1, 93± 11 An LQ R weight selection approach to the discrete generalized H 2 control problem D. A. WILSON², M. A. NEKOUI² and G. D. HALIKIAS² It is known that a generalized
More informationonly nite eigenvalues. This is an extension of earlier results from [2]. Then we concentrate on the Riccati equation appearing in H 2 and linear quadr
The discrete algebraic Riccati equation and linear matrix inequality nton. Stoorvogel y Department of Mathematics and Computing Science Eindhoven Univ. of Technology P.O. ox 53, 56 M Eindhoven The Netherlands
More information4. Matrix inverses. left and right inverse. linear independence. nonsingular matrices. matrices with linearly independent columns
L. Vandenberghe ECE133A (Winter 2018) 4. Matrix inverses left and right inverse linear independence nonsingular matrices matrices with linearly independent columns matrices with linearly independent rows
More informationAlgebra C Numerical Linear Algebra Sample Exam Problems
Algebra C Numerical Linear Algebra Sample Exam Problems Notation. Denote by V a finite-dimensional Hilbert space with inner product (, ) and corresponding norm. The abbreviation SPD is used for symmetric
More informationParametric Signal Modeling and Linear Prediction Theory 1. Discrete-time Stochastic Processes (cont d)
Parametric Signal Modeling and Linear Prediction Theory 1. Discrete-time Stochastic Processes (cont d) Electrical & Computer Engineering North Carolina State University Acknowledgment: ECE792-41 slides
More informationFINITE-DIMENSIONAL models are central to most
1 A convex optimization approach to ARMA modeling Tryphon T. Georgiou and Anders Lindquist Abstract We formulate a convex optimization problem for approximating any given spectral density with a rational
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 informationTime Series Examples Sheet
Lent Term 2001 Richard Weber Time Series Examples Sheet This is the examples sheet for the M. Phil. course in Time Series. A copy can be found at: http://www.statslab.cam.ac.uk/~rrw1/timeseries/ Throughout,
More informationCanonical lossless state-space systems: staircase forms and the Schur algorithm
Canonical lossless state-space systems: staircase forms and the Schur algorithm Ralf L.M. Peeters Bernard Hanzon Martine Olivi Dept. Mathematics School of Mathematical Sciences Projet APICS Universiteit
More informationComplex Analysis Important Concepts
Complex Analysis Important Concepts Travis Askham April 1, 2012 Contents 1 Complex Differentiation 2 1.1 Definition and Characterization.............................. 2 1.2 Examples..........................................
More informationMinimal positive realizations of transfer functions with nonnegative multiple poles
1 Minimal positive realizations of transfer functions with nonnegative multiple poles Béla Nagy Máté Matolcsi Béla Nagy is Professor at the Mathematics Department of Technical University, Budapest, e-mail:
More informationModel reduction via tangential interpolation
Model reduction via tangential interpolation K. Gallivan, A. Vandendorpe and P. Van Dooren May 14, 2002 1 Introduction Although most of the theory presented in this paper holds for both continuous-time
More informationNonlinear 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 informationOn Moving Average Parameter Estimation
On Moving Average Parameter Estimation Niclas Sandgren and Petre Stoica Contact information: niclas.sandgren@it.uu.se, tel: +46 8 473392 Abstract Estimation of the autoregressive moving average (ARMA)
More informationarxiv: v1 [cs.sy] 29 Dec 2018
ON CHECKING NULL RANK CONDITIONS OF RATIONAL MATRICES ANDREAS VARGA Abstract. In this paper we discuss possible numerical approaches to reliably check the rank condition rankg(λ) = 0 for a given rational
More informationRobust-to-Dynamics Linear Programming
Robust-to-Dynamics Linear Programg Amir Ali Ahmad and Oktay Günlük Abstract We consider a class of robust optimization problems that we call robust-to-dynamics optimization (RDO) The input to an RDO problem
More informationDesign of iterative learning control algorithms using a repetitive process setting and the generalized KYP lemma
Design of iterative learning control algorithms using a repetitive process setting and the generalized KYP lemma 22 th July 2015, Dalian, China Wojciech Paszke Institute of Control and Computation Engineering,
More information2. Linear algebra. matrices and vectors. linear equations. range and nullspace of matrices. function of vectors, gradient and Hessian
FE661 - Statistical Methods for Financial Engineering 2. Linear algebra Jitkomut Songsiri matrices and vectors linear equations range and nullspace of matrices function of vectors, gradient and Hessian
More information