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Methods for Solution of Large Optimal Control Problems That Bypass Open-Loop Model Reduction

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Methods for Solution of Large Optimal Control Problems That Bypass Open-Loop Model Reduction Noname manuscript No. (will be inserted by the editor) Methods for solution of large optimal control problems that bypass open-loop model reduction Thomas Bewley · Paolo Luchini · Jan Pralits Received: date / Accepted: date Abstract Three algorithms for efficient solution of op- Keywords Computational mechanics · Optimal timal control problems for high-dimensional systems control · minimum-energy control · subspace iteration are presented. Each bypasses the intermediate (and, un- necessary) step of open-loop model reduction. Each also bypasses the solution of the full Riccati equation cor- 1 Introduction & Background responding to the LQR problem, which is numerically intractable for large n. Motivation for this effort comes A primary difficulty of the linear feedback control prob- from the field of model-based flow control, where open- lem is that its computational complexity scales poorly loop model reduction often fails to capture the dynam- with problem size. Though it is quite routine with mod- ics of interest (governed by the Navier-Stokes equation). ern computers to perform numerical simulations of com- Our Minimum Control Energy (MCE) method is a sim- plex systems with state dimension n ≥ O(106), it is rare plified expression for the well-known minimum-energy to see a feedback control problem solved directly on sys- stabilizing control feedback that depends only on the tems with state dimension larger than O(103). Instead, left eigenvectors corresponding to the unstable eigenval- the most common strategy is a two-step approach: first ues of the system matrix A. Our Adjoint of the Direct- apply some sort of \balanced" open-loop model reduc- Adjoint (ADA) method is based on the repeated itera- tion to the system [16,19], then solve a control problem tive computation of the adjoint of a forward problem, based on this reduced-order model. While this two-step itself defined to be the direct-adjoint vector pair asso- approach proves to be successful for some problems, it ciated with the LQR problem. Our Oppositely-Shifted is problematical for others. Subspace Iteration (OSSI, the main new result of the The reason for this difficulty is twofold. First, though present paper) method is based on our new subspace a balanced truncation of a system accounts for the con- iteration method for computing the Schur vectors cor- trollability and observability of the various eigenmodes responding, notably, to the m n central eigenvalues of the system (i.e., the matrices B and C in the system (near the imaginary axis) of the Hamiltonian matrix model), such a truncation is inherently an \open-loop" related to the Riccati equation of interest. Prototype model reduction strategy, and does not account for the OSSI implementations are tested on a low-order con- closed-loop control objective (i.e., the matrices Q and trol problem to illustrate its behavior. R in the cost function). The second issue is related to the condition of eigen- Thomas Bewley Dept of MAE, UC San Diego, La Jolla CA 92093-0411, USA vector nonorthogonality of the system matrix A. In sys- E-mail: [email protected] tems characterized by this condition, the eigenvalues Paolo Luchini of the system matrix, and the controllability and ob- Universit`adi Salerno, DIIN, 84084 Fisciano, Italy servability of the corresponding eigenvectors, do not E-mail: [email protected] tell the whole story, and very large transfer-function Jan Pralits norms and transient energy growth (a.k.a. \peaking") Universit`adi Genova, DICCA, 16145 Genova, Italy are possible even if all of the eigenvalues of the sys- E-mail: [email protected] tem are stable and \well damped". The mechanism 2 Thomas Bewley et al. for such transient energy growth is the possibility of Toward this end, we may define an adjoint field related initial destructive interference of multiple nonorthog- to the optimization problem of interest, onal eigenvectors of the system [6,4]; this destructive H interference can reduce substantially in time (as dif- −dp=dt = A p + Qx with p(T ) = QT x(T ): (2b) ferent modes decay at different rates), leading to sub- For any initial condition x , the control u on t 2 [0;T ] stantial energy growth in the system, before ultimate 0 which minimizes J(x(u); u) may be found using (2a)- energy decay due to the stability of the correspond- (2b) by iterative state/adjoint computation, starting ing eigenvalues. The energy growth via such mecha- from an initial guess u = 0 on t 2 [0;T ] and at each it- nisms can be several orders of magnitude, and can thus eration updating the control u (using, e.g., a conjugate lead quickly to nonlinear (a.k.a. \secondary") instabil- gradient or BFGS method) based on the gradient ity even when the initial perturbations on the system are quite small. Eigenvector nonorthogonality thus re- DJ(x(u); u)=Du = BH p + Ru; (2c) duces the relevance of eigenmodes considered on their own, and model reduction strategies based on retain- computed using the adjoint field p. ing certain open-loop eigenmodes from the spectrum, To verify the relevance of the adjoint equation (2b) but not others, can lead to significant problems, as the for the minimization of J(x(u); u) in (1) when x is re- full set of eigenvectors necessary to capture the tran- lated to u by (2a), consider a linear perturbation anal- sient energy growth mechanisms present in the system ysis of (1) and (2a): replacing u u + u0, x x + x0, are generally not contained by a model that has been and J J + J 0 and keeping all terms which are linear reduced in such a fashion [4,13]. in the perturbation quantities gives Finally, the two-step approach described above ap- Z T 0 H 0 H 0 H 0 pears to be an unfortunate duplication of effort: an al- J = (x Qx + u Ru ) dt + x (T )QT x (T ) (3a) gorithm with the complexity of an eigenvalue problem is 0 first used to reduce the order of a model, then another and a linear evolution equation for x0: algorithm with the complexity of an eigenvalue prob- 0 0 0 lem is used to solve a control problem based on this Lx = Bu with x (0) = 0 (3b) reduced-order model. The central idea of the present where L , d=dt − A: (3c) paper is to consider control formulations which solve one such eigenvalue problem, not two. R T H Defining the inner product ha; bi = 0 a b dt, we may express an adjoint identity 0 ∗ 0 1.1 Brief review of the optimal control problem hp; Lx i = hL p; x i + b: (3d) Using integration by parts, it follows that As a point of reference for the derivations in the sec- ∗ H H 0 H 0 tions to come, it is necessary to review concisely the L = −d=dt − A ; b = p x jt=T − p x jt=0: (3e) key equations and standard methods of solution of the optimal control problem (written here in its simplest Using L∗ to define an appropriate evolution equation form, which is sufficient for the discussion that follows). for p [which is equivalent to (2b)], This background material is broadly known [5,16], and ∗ is leveraged heavily in the following form in the sec- L p = Qx with p(T ) = QT x(T ); (3f) tions that follow. In short, we seek to minimize a finite- and substituting both (3b) and (3f) into the identity horizon cost function (3d) allows us to rewrite (3a) as Z T 1 H H Z T h iH Z T hDJ iH J(x(u); u) = (x Qx + u Ru) dt J 0 = BH p + Ru u0 dt = u0 dt; (3g) 2 0 (1) 0 0 Du 1 + xH (T )Q x(T ) 2 T from which the gradient in (2c) is readily identified. Rather than using an iterative vector-based method H where Q ≥ 0, R > 0, QT ≥ 0, and (·) denotes the to find the u on t 2 [0;T ] to minimize J(x(u); u) for the conjugate transpose, and where the state x = x(u) is initial condition x0, leveraging (2a)-(2c) as described related to the control u via a linear (or, linearized) time- above, we may instead enforce the condition that varying (LTV), possibly complex system DJ(x(u); u)=Du = 0 directly, thus reducing (2c) to −1 H dx=dt = Ax + Bu with x(0) = x0: (2a) u = −R B p: (4) Methods for solution of large optimal control problems that bypass open-loop model reduction 3 Substituting this condition into (2a) allows us to rewrite where the two-point boundary-value problem (TPBVP) for 2 j j j 3 X ∗ xi the state/adjoint pair fx; pg, as listed in (2a)-(2b), in V = = v1 v2 ::: vn ∗ ; vi = ; (6c) P ∗ 4 5 pi the convenient combined matrix form j j j d x A −BR−1BH x = (5a) dt p −Q −AH p and the eigenvalues of Z on the main diagonal of the |{z} | {z } |{z} upper triangular (or diagonal) matrix T are enumerated v Z v such that the LHP eigenvalues appear first, followed by with initial and terminal conditions the RHP eigenvalues (that is, we assume that Z has ( no eigenvalues on the imaginary axis). Defining y = x = x0 at t = 0; (5b) V −1v, it follows from (5a) and (6b) that dy=dt = T y. p = Q x at t = T; T The stable solutions of y are spanned by the first n which may be solved for arbitrary initial conditions x0 columns of T (that is, they are nonzero only in the first by the sweep method: assuming a relation exists be- n elements of y).
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