American Institute of Mathematical Sciences

Advanced
2016, 6(4): 413-435. doi: 10.3934/naco.2016018

A POD projection method for large-scale algebraic Riccati equations

Boris Kramer 1, and  John R. Singler 2,

1. 

Department of Aeronautics and Astronautics, Massachusetts Institute of Technology,Cambridge, MA 02139, United States
2. 

Department of Mathematics and Statistics, Missouri University of Science and Technology, Rolla, MO 65409-0020, United States

Received  February 2016 Revised  September 2016 Published  December 2016

The solution of large-scale matrix algebraic Riccati equations is important for instance in control design and model reduction and remains an active area of research. We consider a class of matrix algebraic Riccati equations (AREs) arising from a linear system along with a weighted inner product. This problem class often arises from a spatial discretization of a partial differential equation system. We propose a projection method to obtain low rank solutions of AREs based on simulations of linear systems coupled with proper orthogonal decomposition. The method can take advantage of existing (black box) simulation code to generate the projection matrices. We also develop new weighted norm residual computations and error bounds. We present numerical results demonstrating that the proposed approach can produce highly accurate approximate solutions. We also briefly discuss making the proposed approach completely data-based so that one can use existing simulation codes without accessing system matrices.
Keywords: Algebraic Riccati equations, large-scale, proper orthogonal decomposition, control theory., reduced-order modeling.
Mathematics Subject Classification: Primary: 49; Secondary: 6.
Citation: Boris Kramer, John R. Singler. A POD projection method for large-scale algebraic Riccati equations. Numerical Algebra, Control & Optimization, 2016, 6 (4) : 413-435. doi: 10.3934/naco.2016018
References:
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show all references

References:
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[9]

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[10]

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[11]

C. A. Beattie, J. Borggaard, S. Gugercin and T. Iliescu, A domain decomposition approach to POD,, in Proceedings of the 45th IEEE Conference on Decision and Control, (2006).   Google Scholar

[12]

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[13]

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[14]

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[15]

P. Benner and J. Saak, Numerical solution of large and sparse continuous time algebraic matrix Riccati and Lyapunov equations: a state of the art survey,, GAMM-Mitteilungen, 36 (2013), 32.  doi: 10.1002/gamm.201310003.  Google Scholar

[16]

P. Benner and Z. Bujanović, On the solution of large-scale algebraic Riccati equations by using low-dimensional invariant subspaces,, Linear Algebra and its Applications, 488 (2016), 430.  doi: 10.1016/j.laa.2015.09.027.  Google Scholar

[17]

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[18]

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[19]

J. Borggaard and M. Stoyanov, An efficient long-time integrator for Chandrasekhar equations,, in Proceedings of the 47th IEEE Conference on Decision and Control, (2008), 3983.   Google Scholar

[20]

J. A. Burns and K. P. Hulsing, Numerical methods for approximating functional gains in LQR boundary control problems,, Math. Comput. Modelling, 33 (2001), 89.  doi: 10.1016/S0895-7177(00)00231-4.  Google Scholar

[21]

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[22]

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[23]

J. A. Burns, Introduction to theCalculus of Variations and Control-With Modern Applications,, CRC Press, (2014).   Google Scholar

[24]

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[25]

S. Chaturantabut and D. C. Sorensen, Discrete empirical interpolation for nonlinear model reduction,, in Proceedings of the 48th IEEE Conference on Decision and Control, (2009), 4316.   Google Scholar

[26]

K. K. Chen and C. W. Rowley, Fluid flow control applications of H2 optimal actuator and sensor placement,, in Proceedings of the American Control Conference, (2014), 4044.   Google Scholar

[27]

K. Chen and C. W. Rowley, H2 optimal actuator and sensor placement in the linearised complex Ginzburg-Landau system,, J. Fluid Mech., 681 (2011), 241.  doi: 10.1017/jfm.2011.195.  Google Scholar

[28]

N. Darivandi, K. Morris and A. Khajepour, An algorithm for LQ optimal actuator location,, Smart Materials and Structures, 22 (2013).   Google Scholar

[29]

B. T. Dickinson and J. R. Singler, Nonlinear model reduction using group proper orthogonal decomposition,, Int. J. Numer. Anal. Model., 7 (2010), 356.   Google Scholar

[30]

V. Druskin and L. Knizhnerman, Extended Krylov subspaces: approximation of the matrix square root and related functions,, SIAM Journal on Matrix Analysis and Applications, 19 (1998), 755.  doi: 10.1137/S0895479895292400.  Google Scholar

[31]

M. Fahl, Computation of POD basis functions for fluid flows with Lanczos methods,, Math. Comput. Modelling, 34 (2001), 91.  doi: 10.1016/S0895-7177(01)00051-6.  Google Scholar

[32]

J. S. Gibson and A. Adamian, Approximation theory for linear-quadratic-Gaussian optimal control of flexible structures,, SIAM J. Control Optim., 29 (1991), 1.  doi: 10.1137/0329001.  Google Scholar

[33]

L. Giraud, J. Langou, M. Rozložník and J. van den Eshof, Rounding error analysis of the classical Gram-Schmidt orthogonalization process,, Numer. Math., 101 (2005), 87.  doi: 10.1007/s00211-005-0615-4.  Google Scholar

[34]

G. Golub and C. F. Van Loan, Matrix Computations,, Johns Hopkins University, ().   Google Scholar

[35]

A. Hay, J. Borggaard and D. Pelletier, Local improvements to reduced-order models using sensitivity analysis of the proper orthogonal decomposition,, J. Fluid Mech., 629 (2009), 41.  doi: 10.1017/S0022112009006363.  Google Scholar

[36]

M. Heyouni and K. Jbilou, An extended block Arnoldi algorithm for large-scale solutions of the continuous-time algebraic Riccati equation,, Electron. Trans. Numer. Anal., 33 (2009), 53.   Google Scholar

[37]

P. Holmes, J. L. Lumley, G. Berkooz and C. W. Rowley, Turbulence, Coherent Structures, Dynamical Systems and Symmetry,, 2nd edition, (2012).  doi: 10.1017/CBO9780511919701.  Google Scholar

[38]

I. Jaimoukha and E. Kasenally, Krylov subspace methods for solving large Lyapunov equations,, SIAM J. Numer. Anal., 31 (1994), 227.  doi: 10.1137/0731012.  Google Scholar

[39]

K. Jbilou, Block Krylov subspace methods for large algebraic Riccati equations,, Numerical Algorithms, 34 (2003), 339.  doi: 10.1023/B:NUMA.0000005349.18793.28.  Google Scholar

[40]

K. Jbilou, An Arnoldi based algorithm for large algebraic Riccati equations,, Applied Mathematics Letters, 19 (2006), 437.  doi: 10.1016/j.aml.2005.07.001.  Google Scholar

[41]

K. Jbilou and A. Riquet, Projection methods for large Lyapunov matrix equations,, Linear Algebra and its Applications, 415 (2006), 344.  doi: 10.1016/j.laa.2004.11.004.  Google Scholar

[42]

T. Kailath, Some Chandrasekhar-type algorithms for quadratic regulators,, in Proceedings of the 1972 IEEE Conference on Decision and Control and 11th Symposium on Adaptive Processes., (1972), 219.   Google Scholar

[43]

D. Kasinathan and K. Morris, H∞-optimal actuator location,, IEEE Trans. Automat. Control, 58 (2013), 2522.  doi: 10.1109/TAC.2013.2266870.  Google Scholar

[44]

C. Kenney, A. Laub and M. Wette, Error bounds for Newton refinement of solutions to algebraic Riccati equations,, Mathematics of Control, 3 (1990), 211.  doi: 10.1007/BF02551369.  Google Scholar

[45]

B. Kramer, Solving algebraic Riccati equations via proper orthogonal decomposition,, in Proceedings of the 19th IFAC World Congress, (2014), 7767.   Google Scholar

[46]

K. Kunisch and S. Volkwein, Galerkin proper orthogonal decomposition methods for a general equation in fluid dynamics,, SIAM Journal on Numerical analysis, 40 (2002), 492.  doi: 10.1137/S0036142900382612.  Google Scholar

[47]

H. Kwakernaak and R. Sivan, Linear Optimal Control Systems,, Wiley-Interscience, (1972).   Google Scholar

[48]

C. H. Lee and H. T. Tran, Reduced-order-based feedback control of the Kuramoto-Sivashinsky equation,, J. Comput. Appl. Math., 173 (2005), 1.  doi: 10.1016/j.cam.2004.02.021.  Google Scholar

[49]

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