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# Model Reduction of Port-Hamiltonian Systems as ... - Conferences.hu

Model Reduction of Port-Hamiltonian Systems as ... - Conferences.hu

## R. V. Polyuga and A. J.

R. V. Polyuga and A. J. van der Schaft • Model Reduction of Port-Hamiltonian Systems as Structured Systems Hamiltonian structure for the reduced order model, and, as a consequence, passivity. A. System representation using differential operators In order to proceed we need the following notation. Let K(s), P(s) be polynomial matrices in s: K(s) = P(s) = l∑ K j s j , K j ∈ R n×n , j=0 l∑ P j s j , P j ∈ R n×m , j=0 where K is invertible, K −1 P is a strictly proper rational matrix and l is the order of the system (l = 1 for (1) and l = 2 for (2)). Then K( d dt ), P( d dt ) denote the differential operators l∑ K( d dt ) = d j l∑ K j dt j , P( d dt ) = d j P j dt j . j=0 j=0 The systems (without a feed-through term) can be now defined by the following set of equations: { K( d dt Σ : )x = P( d dt )u, (4) y = Cx, where C ∈ R p×n . This is a more general representation of (1), (2), which allows for derivatives of the input u. B. Reachability Gramian Recall from [1] that for the first order stable system (1) the corresponding (infinite) reachability Gramian is defined as W := ∫ ∞ 0 e Aτ BB T e AT τ dτ. (5) This Gramian is one of the central objects in the mathematical systems theory. It is a symmetric positive semidefinite matrix which satisfies the following Lyapunov equation AW + WA T + BB T = 0. (6) The eigenvalues of the Gramian W are measures of the reachability of the system (1). The Gramian (5) can be rewritten as W := ∫ ∞ 0 x(t)x(t) T dt (7) for x(t) being the state of the corresponding (first order) system when the input u is the δ-distribution. Indeed, the solution of ẋ = Ax + Bu, x(0) = 0, to the input u(t) = Iδ(t) is given as x(t) = e At B. In a similar way as in (7) the reachability Gramians of higher order systems can be defined. In particular, the reachability Gramian of the second order system (2) can be shown to be the left upper block of the reachability Gramian of the corresponding first order system (1). Using Parseval’s theorem, the Gramian (7) can be considered in the frequency domain: W = 1 2π ∫ ∞ −∞ x(iω)x(iω) ∗ dt, (8) where the star denotes the conjugate transpose and x(iω) is the Laplace transform of the time signal x(t) (for simplicity of notation, quantities in the time and frequency domains are denoted by the same symbol x). The transfer function of (4) in the frequency domain is given as G(s) = CK(s) −1 P(s), while the input-to-state and the input-to-output maps are x(s) = K(s) −1 P(s)u(s), y(s) = G(s)u(s). For the input being the unit impulse u(t) = δ(t)I it follows that u(s) = I and the about expressions read x(s) = K(s) −1 P(s), y(s) = G(s). In the time domain we have trace { ∫ ∞ y(t)y(t) T dt } = trace { ∫ ∞ Cx(t)x(t) T C T dt } 0 Using the notation = trace { CWC T} . F(s) := K(s) −1 P(s) and the Parceval’s theorem we obtain for the frequency domain trace { ∫ ∞ y(t)y(t) T dt } = 0 trace { C ( 1 2π ∫ ∞ −∞ 0 F(iω)F(iω) ∗ dω ) C T} . This reasoning results in the conclusion that the reachability Gramian of a system with the corresponding order is given in the frequency domain as ∫ ∞ W = 1 2π −∞ C. Model reduction procedure F(iω)F(iω) ∗ dω. (9) Model reduction of the systems (4), as explained in [19], is based on the projection of (4) on the dominant eigenspace of a Gramian W of the state x. The eigenvalue decomposition of the corresponding Gramian W gives W = V ΛV T , Λ = diag(Λ 1 , Λ 2 ). (10) where Λ ∈ R n×n is a diagonal matrix containing the real 1510

Proceedings of the 19th International Symposium on Mathematical Theory of Networks and Systems – MTNS 2010 • 5–9 July, 2010 • Budapest, Hungary eigenvalues of the Gramian W in decreasing order, and V ∈ R n×n is an orthogonal matrix. Choosing the dimension of the reduced order model r leads to the partitioning Λ = diag(Λ 1 , Λ 2 ), V = [V 1 , V 2 ], (11) where Λ 1 ∈ R r×r , Λ 2 , ∈ R (n−r)×(n−r) , V 1 ∈ R n×r , V 2 ∈ R n×(n−r) . An orthogonal basis for the dominant eigenspace of dimension r is used to construct a reduced order model: { ˆK11 ( ˆΣ d dt : )ˆx = ˆP( d dt )u, ŷ = Ĉ1ˆx, (12) where ˆx ∈ R r , Ĉ 1 = CV 1 ∈ R p×r , ˆK 11 ( d dt ) = l∑ ˆP( d dt ) = l∑ j=0 j=0 ˆK j d j dt j , ˆP j d j dt j , ˆKj = V T 1 K j V 1 ∈ R r×r , ˆPj = V T 1 P j ∈ R r×m . This model reduction method by construction preserves the second or higher order structure of the full order model Σ in (4) for the reduced order model in (12) Suppose the polynomial matrix ˆK(s) has the following splitting corresponding to the dimension of the reduced order model [ ] V T ˆK(s) = V T 1 K(s)V 1 V1 T K(s)V 2 K(s)V = V2 TK(s)V 1 V2 TK(s)V 2 [ ] ˆK11 (s) ˆK12 (s) =: . ˆK 21 (s) ˆK22 (s) Let L(s) be the polynomial matrix L(s) := ( ˆK 11 (s)) −1 ˆK12 (s). (13) If the reduced order system has no poles on the imaginary axis, sup ‖L(iw)‖ 2 is finite. Then the model reduction ω method results in the following H 2 error bound. Theorem 1: [19] Consider the full order structured system Σ in (4) and the reduced order structured system ˆΣ in (12). Then the error system E = Σ − ˆΣ satisfies the following H 2 error bound ‖E‖ 2 H 2 trace{Ĉ2Λ 2 Ĉ T 2 } + κ trace{Λ 2 }, (14) where κ is a constant depending on Σ, ˆΣ, and the diagonal elements of Λ 2 are the neglected smallest eigenvalues of W : κ = sup ‖(Ĉ1L(iω)) ∗ (Ĉ1L(iω) − 2Ĉ2)‖ 2 , ω Ĉ 2 = CV 2 . The frequency domain representation of the Gramian (9) results in the following expressions [19] in the coordinates, where the Gramian is diagonal W = Λ: ∫ ∞ Λ = 1 2π Λ 1 = 1 2π Λ 2 = 1 2π 0 = 1 2π −∞ ∫ ∞ −∞ ∫ ∞ −∞ ∫ ∞ −∞ ˆF(iω) ˆF(iω) ∗ dω, ˆF 1 (iω) ˆF 1 (iω) ∗ dω, ˆF 2 (iω) ˆF 2 (iω) ∗ dω, ˆF 2 (iω) ˆF 1 (iω) ∗ dω, (15) where ˆF 1 (s), ˆF2 (s) come from the splitting according to the dimension of the reduced order model of ˆF(s), which is nothing but the defined before matrix F(s) in the new coordinates: [ ] ˆF1 (s) ˆF(s) = = V ˆF T F(s) = V T K(s) −1 P(s). (16) 2 (s) The expressions (15) are of the direct use in the proof of the error bound in Theorem 1. The proof of Theorem 1 can be found in [19] and is also sketched in [14]. III. APPLICATION OF THE METHOD TO PORT-HAMILTONIAN SYSTEMS Consider linear port-Hamiltonian systems [20], [7] { ẋ = (J − R)Qx + Bu, Σ PHS : y = B T Qx. (17) As discussed in [15], [14], there exists a coordinate transformation S, x = Sx I , such that in the new coordinates Q I = S T QS = I. (18) By defining the transformed system matrices as J I = S −1 JS −T , R I = S −1 RS −T , B I = S −1 B, we obtain the transformed port-Hamiltonian system { ẋI = (J I − R I )x I + B I u, y = BI Tx (19) I, with energy H(x I ) = 1 2 ‖x I‖ 2 . System (19) can be rewritten as { IẋI − (J I − R I )x I = B I u, y = BI Tx (20) I, which is of the form (4) with K( d dt ) = I d dt − (J I − R I ), P( d dt ) = B I, C = B T I . The Gramian of the transformed port-Hamiltonian system (20) W := ∫ ∞ 0 x I (t)x I (t) T dt (21) 1511

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