March  2018, 8(1): 17-46. doi: 10.3934/naco.2018002

Fourier-splitting method for solving hyperbolic LQR problems

1. 

Institute of Mathematics, Eötvös Loránd University Budapest, MTA-ELTE Numerical Analysis and Large Networks Research Group, Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary

2. 

School of Mathematical Sciences and Information Technology, Yachay Tech, Hacienda San José y Proyecto Yachay, EC100650 Urcuquí, Ecuador

3. 

Department of Mathematics, University of Innsbruck, Technikerstrasse 13, A-6020 Innsbruck, Austria

* Corresponding author: P. Csomós

Received  December 2016 Revised  November 2017 Published  March 2018

Fund Project: The first author is supported by the National Research, Development and Innovation Fund (Hungary) under the grant PD121117.

We consider the numerical approximation to linear quadratic regulator problems for hyperbolic partial differential equations where the dynamics is driven by a strongly continuous semigroup. The optimal control is given in feedback form in terms of Riccati operator equations. The computational cost relies on solving the associated Riccati equation and computing the optimal state. In this paper we propose a novel approach based on operator splitting idea combined with Fourier's method to efficiently compute the optimal state. The Fourier's method allows to accurately approximate the exact flow making our approach computational efficient. Numerical experiments in one and two dimensions show the performance of the proposed method.

Citation: Petra Csomós, Hermann Mena. Fourier-splitting method for solving hyperbolic LQR problems. Numerical Algebra, Control & Optimization, 2018, 8 (1) : 17-46. doi: 10.3934/naco.2018002
References:
[1]

H. Abou-Kandil, G. Freiling, V. Ionescu and G. Jank, Matrix Riccati Equations in Control and Systems Theory, Birkhäuser, Basel, Switzerland, 2003. Google Scholar

[2]

A. H. Al-Mohy and N. J. Higham, Computing the action of the matrix exponential, with an application to exponential integrators, SIAM J. Sci. Comput., 33 (2011), 488-511.   Google Scholar

[3]

E. AriasV. HernándezJ. Ibanes and J. Peinado, A family of BDF algorithms for solving differential matrix Riccati equations using adaptive techniques, Procedia Computer Science, 1 (2010), 2569-2577.   Google Scholar

[4]

E. Armstrong, An extension of Bass' algorithm for stabilizing linear continuous constant systems, IEEE Trans. Automatic Control, AC-20 (1975), 153-154.   Google Scholar

[5]

A. Balakrishnan, Applied Functional Analysis, Springer-Verlag, New York, 1981.  Google Scholar

[6]

H. BanksR. Smith and Y. Wang, The modeling of piezoceramic patch interactions with shells, plates and beams, Quart. Appl. Math., 53 (1995), 353-381.   Google Scholar

[7]

A. BátkaiP. CsomósB. Farkas and G. Nickel, Operator splitting for non-autonomous evolution equations, J. Funct. Anal., 260 (2011), 2163-2192.   Google Scholar

[8]

A. BátkaiP. Csomós and G. Nickel, Operator splittings and spatial approximations for evolution equations, J. Evol. Eqs., 9 (2009), 613-636.   Google Scholar

[9]

A. Bensoussan, G. Da Prato, M. Delfour and S. Mitter, Representation and Control of Infinite Dimensional Systems, Birkhäuser, 1993.  Google Scholar

[10]

P. BennerP. EzzattiH. MenaE. S. Quintana-Ortí and A. Remón, Solving matrix equations on multi-core and many-core architectures, Algorithms, 6 (2013), 857-870.   Google Scholar

[11]

P. Benner and H. Mena, Numerical solution of the infinite-dimensional LQR-problem and the associated differential Riccati equations, Journal of Numerical Mathematics (2016), in press.  Google Scholar

[12]

P. Benner and H. Mena, Rosenbrock methods for solving differential Riccati equations, IEEE Transactions on Automatic Control, 58 (2013), 2950-2957.   Google Scholar

[13]

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), 32-52.   Google Scholar

[14]

P. Csomós and J. Winckler, A semigroup proof for the well-posedness of the linearised shallow water equations, J. Anal. Math., 43 (2017), 445-459.   Google Scholar

[15]

G. Da Prato, Direct solution of a Riccati equation arising in stochastic control theory, Appl. Math. Optim., 11 (1984), 191-208.   Google Scholar

[16]

G. Da Prato, P. Kunstmann, I. Lasiecka, A. Lunardi, R. Schnaubelt and L. Weis, Functional Analytic Methods for Evolution Equations, Springer-Verlag, Berlin, 2004.  Google Scholar

[17]

K. Engel and R. Nagel, One-Parameter Semigroups for Linear Evolution Equations, Graduate Texts in Mathematics, Springer-Verlag, New York, 2000.  Google Scholar

[18]

F. Flandoli, Direct solution of a Riccati equation arising in a stochastic control problem with control and observation on the boundary, Appl. Math. Optim., 14 (1986), 107-129.   Google Scholar

[19]

C. HafizogluI. LasieckaT. LevajkovićH. Mena and A. Tuffaha, The stochastic linear quadratic problem with singular estimates, SIAM J. Control Optim., 55 (2017), 595-626.   Google Scholar

[20]

E. Hansen and A. Ostermann, Exponential splitting for unbounded operators, Math. Comput., 78 (2009), 1485-1496.   Google Scholar

[21]

A. Ichikawa, Dynamic programming approach to stochastic evolution equation, SIAM J. Control. Optim., 17 (1979), 152-174.   Google Scholar

[22]

A. Ichikawa and H. Katayama, Remarks on the time-varying H Riccati equations, Sys. Cont. Lett., 37 (1999), 335-345.   Google Scholar

[23]

O. Iftime and M. Opmeer, A representation of all bounded selfadjoint solutions of the algebraic Riccati equation for systems with an unbounded observation operator, Proceedings of the 43rd IEEE Conference on Decision and Control, Atlantis, Paradise Island, Bahamas, December 14-07 (2004), 2865-2870. Google Scholar

[24]

K. Ito and F. Kappel, Evolution Equations and Approximations, World Scientific, Singapore, 2002. Google Scholar

[25]

T. Jahnke and Ch. Lubich, Error bounds for exponential operator splittings, BIT, 40 (2000), 735-744.   Google Scholar

[26]

D. Kleinman, On an iterative technique for Riccati equation computations, IEEE Trans. Automatic Control, AC-13 (1968), 114-115.   Google Scholar

[27]

A. Kofler, H. Mena and A. Ostermann, Splitting methods for stochastic partial differential equations, preprint Google Scholar

[28]

N. LangH. Mena and J. Saak, On the benefits of the LDL factorization for large-scale differential matrix equation solvers, Linear Algebra and its Applications, 480 (2015), 44-71.   Google Scholar

[29]

I. Lasiecka, Optimal control problems and Riccati equations for systems with unbounded controls and partially analytic generators: applications to boundary and point control problems, in Functional Analytic Methods for Evolution Equations (eds. M. Iannelli, R. Nagel, S. Piazzera), Lecture Notes in Mathematics, Springer, Berlin, Heidelberg, 1855 (2004), 313-369.  Google Scholar

[30]

I. Lasiecka and R. Triggiani, Control Theory for Partial Differential Equations: Continuous and Approximation Theories Ⅱ. Abstract Hyperbolic-like Systems over a Finite Time Horizon, Cambridge University Press, Cambridge, UK, 2000.  Google Scholar

[31]

I. Lasiecka and A. Tuffaha, Riccati equations for the Bolza problem arising in boundary/point control problems governed by $ C_{0} $-semigroups satisfying a singular estimate, J. Optim. Theory Appl., 136 (2008), 229-246.   Google Scholar

[32]

T. Levajković and H. Mena, On deterministic and stochastic linear quadratic control problem, in Current Trends in Analysis and Its Applications. Trends in Mathematics. (eds. V. Mityushev, M. Ruzhansky), Birkhäuser, Cham, (2015), 315-322.  Google Scholar

[33]

T. LevajkovićH. Mena and A. Tuffaha, The stochastic linear quadratic control problem: A chaos expansion approach, Evolution Equations and Control Theory, 5 (2016), 105-134.   Google Scholar

[34]

T. LevajkovićH. Mena and A. Tuffaha, A numerical approximation framework for the stochastic linear quadratic regulator problem on Hilbert spaces, Applied Mathematics and Optimization, 75 (2017), 499-523.   Google Scholar

[35]

V. Mehrmann, The Autonomous Linear Quadratic Control Problem, Springer-Verlag, Berlin, 1991.  Google Scholar

[36]

J. Pedlosky, Geophysical Fluid Dynamics, Springer-Verlag, New York, 1987. Google Scholar

[37]

I. Petersen, V. Ugrinovskii and A. Savkin, Robust Control Design Using H Methods, Springer-Verlag, London, 2000.  Google Scholar

show all references

References:
[1]

H. Abou-Kandil, G. Freiling, V. Ionescu and G. Jank, Matrix Riccati Equations in Control and Systems Theory, Birkhäuser, Basel, Switzerland, 2003. Google Scholar

[2]

A. H. Al-Mohy and N. J. Higham, Computing the action of the matrix exponential, with an application to exponential integrators, SIAM J. Sci. Comput., 33 (2011), 488-511.   Google Scholar

[3]

E. AriasV. HernándezJ. Ibanes and J. Peinado, A family of BDF algorithms for solving differential matrix Riccati equations using adaptive techniques, Procedia Computer Science, 1 (2010), 2569-2577.   Google Scholar

[4]

E. Armstrong, An extension of Bass' algorithm for stabilizing linear continuous constant systems, IEEE Trans. Automatic Control, AC-20 (1975), 153-154.   Google Scholar

[5]

A. Balakrishnan, Applied Functional Analysis, Springer-Verlag, New York, 1981.  Google Scholar

[6]

H. BanksR. Smith and Y. Wang, The modeling of piezoceramic patch interactions with shells, plates and beams, Quart. Appl. Math., 53 (1995), 353-381.   Google Scholar

[7]

A. BátkaiP. CsomósB. Farkas and G. Nickel, Operator splitting for non-autonomous evolution equations, J. Funct. Anal., 260 (2011), 2163-2192.   Google Scholar

[8]

A. BátkaiP. Csomós and G. Nickel, Operator splittings and spatial approximations for evolution equations, J. Evol. Eqs., 9 (2009), 613-636.   Google Scholar

[9]

A. Bensoussan, G. Da Prato, M. Delfour and S. Mitter, Representation and Control of Infinite Dimensional Systems, Birkhäuser, 1993.  Google Scholar

[10]

P. BennerP. EzzattiH. MenaE. S. Quintana-Ortí and A. Remón, Solving matrix equations on multi-core and many-core architectures, Algorithms, 6 (2013), 857-870.   Google Scholar

[11]

P. Benner and H. Mena, Numerical solution of the infinite-dimensional LQR-problem and the associated differential Riccati equations, Journal of Numerical Mathematics (2016), in press.  Google Scholar

[12]

P. Benner and H. Mena, Rosenbrock methods for solving differential Riccati equations, IEEE Transactions on Automatic Control, 58 (2013), 2950-2957.   Google Scholar

[13]

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), 32-52.   Google Scholar

[14]

P. Csomós and J. Winckler, A semigroup proof for the well-posedness of the linearised shallow water equations, J. Anal. Math., 43 (2017), 445-459.   Google Scholar

[15]

G. Da Prato, Direct solution of a Riccati equation arising in stochastic control theory, Appl. Math. Optim., 11 (1984), 191-208.   Google Scholar

[16]

G. Da Prato, P. Kunstmann, I. Lasiecka, A. Lunardi, R. Schnaubelt and L. Weis, Functional Analytic Methods for Evolution Equations, Springer-Verlag, Berlin, 2004.  Google Scholar

[17]

K. Engel and R. Nagel, One-Parameter Semigroups for Linear Evolution Equations, Graduate Texts in Mathematics, Springer-Verlag, New York, 2000.  Google Scholar

[18]

F. Flandoli, Direct solution of a Riccati equation arising in a stochastic control problem with control and observation on the boundary, Appl. Math. Optim., 14 (1986), 107-129.   Google Scholar

[19]

C. HafizogluI. LasieckaT. LevajkovićH. Mena and A. Tuffaha, The stochastic linear quadratic problem with singular estimates, SIAM J. Control Optim., 55 (2017), 595-626.   Google Scholar

[20]

E. Hansen and A. Ostermann, Exponential splitting for unbounded operators, Math. Comput., 78 (2009), 1485-1496.   Google Scholar

[21]

A. Ichikawa, Dynamic programming approach to stochastic evolution equation, SIAM J. Control. Optim., 17 (1979), 152-174.   Google Scholar

[22]

A. Ichikawa and H. Katayama, Remarks on the time-varying H Riccati equations, Sys. Cont. Lett., 37 (1999), 335-345.   Google Scholar

[23]

O. Iftime and M. Opmeer, A representation of all bounded selfadjoint solutions of the algebraic Riccati equation for systems with an unbounded observation operator, Proceedings of the 43rd IEEE Conference on Decision and Control, Atlantis, Paradise Island, Bahamas, December 14-07 (2004), 2865-2870. Google Scholar

[24]

K. Ito and F. Kappel, Evolution Equations and Approximations, World Scientific, Singapore, 2002. Google Scholar

[25]

T. Jahnke and Ch. Lubich, Error bounds for exponential operator splittings, BIT, 40 (2000), 735-744.   Google Scholar

[26]

D. Kleinman, On an iterative technique for Riccati equation computations, IEEE Trans. Automatic Control, AC-13 (1968), 114-115.   Google Scholar

[27]

A. Kofler, H. Mena and A. Ostermann, Splitting methods for stochastic partial differential equations, preprint Google Scholar

[28]

N. LangH. Mena and J. Saak, On the benefits of the LDL factorization for large-scale differential matrix equation solvers, Linear Algebra and its Applications, 480 (2015), 44-71.   Google Scholar

[29]

I. Lasiecka, Optimal control problems and Riccati equations for systems with unbounded controls and partially analytic generators: applications to boundary and point control problems, in Functional Analytic Methods for Evolution Equations (eds. M. Iannelli, R. Nagel, S. Piazzera), Lecture Notes in Mathematics, Springer, Berlin, Heidelberg, 1855 (2004), 313-369.  Google Scholar

[30]

I. Lasiecka and R. Triggiani, Control Theory for Partial Differential Equations: Continuous and Approximation Theories Ⅱ. Abstract Hyperbolic-like Systems over a Finite Time Horizon, Cambridge University Press, Cambridge, UK, 2000.  Google Scholar

[31]

I. Lasiecka and A. Tuffaha, Riccati equations for the Bolza problem arising in boundary/point control problems governed by $ C_{0} $-semigroups satisfying a singular estimate, J. Optim. Theory Appl., 136 (2008), 229-246.   Google Scholar

[32]

T. Levajković and H. Mena, On deterministic and stochastic linear quadratic control problem, in Current Trends in Analysis and Its Applications. Trends in Mathematics. (eds. V. Mityushev, M. Ruzhansky), Birkhäuser, Cham, (2015), 315-322.  Google Scholar

[33]

T. LevajkovićH. Mena and A. Tuffaha, The stochastic linear quadratic control problem: A chaos expansion approach, Evolution Equations and Control Theory, 5 (2016), 105-134.   Google Scholar

[34]

T. LevajkovićH. Mena and A. Tuffaha, A numerical approximation framework for the stochastic linear quadratic regulator problem on Hilbert spaces, Applied Mathematics and Optimization, 75 (2017), 499-523.   Google Scholar

[35]

V. Mehrmann, The Autonomous Linear Quadratic Control Problem, Springer-Verlag, Berlin, 1991.  Google Scholar

[36]

J. Pedlosky, Geophysical Fluid Dynamics, Springer-Verlag, New York, 1987. Google Scholar

[37]

I. Petersen, V. Ugrinovskii and A. Savkin, Robust Control Design Using H Methods, Springer-Verlag, London, 2000.  Google Scholar

Figure 1.  Solution $w(t, \xi)$ to the one-dimensional advection equation (7) without control ($B = 0$) at time $t = 3.2$ by using Godunov's scheme with time step $\tau = 10^{-3}$
Figure 2.  Solution $w(t, \xi)$ to the one-dimensional advection equation (7) without control ($B = 0$) at time $t = 3.2$ by using Lax-Wendroff scheme (right panel) with time step $\tau = 10^{-3}$
Figure 3.  Volume ratio $\mathcal V_1(t)$ of the one-dimensional advection equation (7) without control ($B = 0$) by using Godunov's scheme or Lax-Wendroff scheme with time step $\tau = 10^{-3}$
Figure 4.  Solution $w(t, \xi)$ to the one-dimensional advection equation (7) with distributed control ($B = \text{I}_{\mathcal U}$) at time $t = 3.2$ by using Godunov's scheme with time step $\tau = 10^{-3}$
Figure 5.  Solution $w(t, \xi)$ to the one-dimensional advection equation (7) with distributed control ($B = \text{I}_{\mathcal U}$) at time $t = 3.2$ by using Lax-Wendroff scheme with time step $\tau = 10^{-3}$
Figure 6.  Volume ratio $\mathcal V_1(t)$ of the one-dimensional advection equation (7) with distributed control ($B = \text{I}_{\mathcal U}$) at time $t = 3.2$ by using Godunov's scheme or Lax-Wendroff scheme with time step $\tau = 10^{-3}$
Figure 7.  Solution $w(t, \xi)$ to the one-dimensional advection equation (7) with sink-like control ($B = \text{I}_{\Gamma_1}$) at time $t = 3.2$ by using Godunov's scheme with time step $\tau = 10^{-3}$
Figure 8.  Solution $w(t, \xi)$ to the one-dimensional advection equation (7) with sink-like control ($B = \text{I}_{\Gamma_1}$) at time $t = 3.2$ by using Lax-Wendroff scheme with time step $\tau = 10^{-3}$
Figure 9.  Volume ratio $\mathcal V_1(t)$ of the one-dimensional advection equation (7) with sink-like control ($B = \text{I}_{\Gamma_1}$) at time $t = 3.2$ by using Godunov's scheme with time step $\tau = 10^{-3}$
Figure 10.  Volume ratio $\mathcal V_1(t)$ of the one-dimensional advection equation (7) with sink-like control ($B = \text{I}_{\Gamma_1}$) at time $t = 3.2$ by using Lax-Wendroff scheme with time step $\tau = 10^{-3}$
Figure 9">Figure 11.  Enlargements of Figure 9
Figure 10">Figure 12.  Enlargements of Figure 10
Figure 13.  Solution to two-dimensional advection equation (8) at time $t = 3$ without control ($B = 0$) by using Fourier's method (left column) and Lax-Wendroff scheme (right column) with time step $\tau = 10^{-3}$ and number of grid points $N_\xi = N_\eta = 32, 64,128$ from top to bottom, respectively
Figure 14.  Solution to two-dimensional advection equation (8) at time $t = 3$ with distributed control ($B = \text{I}_{\mathcal U}$) by using Lax-Wendroff scheme (left column) and Fourier-Splitting (right column) with time step $\tau = 10^{-3}$ and number of grid points $N_\xi = N_\eta = 32, 64$ from top to bottom, respectively
Figure 15.  Volume ratio $\mathcal V_2(t)$ of two-dimensional advection equation (8) with distributed control ($B = \text{I}_{\mathcal U}$) by using Lax-Wendroff scheme and Fourier-Splitting with time step $\tau = 10^{-3}$
Figure 16.  Solution to the one-dimensional linearized shallow water equations (9) without control ($B = 0$) at time $t = 25$ with time step $\tau = 10^{-4}$
Figure 17.  Time-evolution of the volume ratio $\mathcal V_3$ for the one-dimensional linearized shallow water equations (9) without control ($B = 0$) at time $t = 25$ with time step $\tau = 10^{-4}$
Figure 18.  Solution to the one-dimensional linearized shallow water equations (9) with distributed control ($B = \text{I}_{\mathcal U}$) at time $t = 25$ with time step $\tau = 10^{-4}$
Figure 19.  Time-evolution of the volume ratio $\mathcal V_3$ for the one-dimensional linearized shallow water equations (9) with distributed control ($B = \text{I}_{\mathcal U}$) at time $t = 25$ with time step $\tau = 10^{-4}$
Figure 20.  Solution to the one-dimensional linearized shallow water equations (9) with sink-like control ($B = \text{I}_{\Gamma_1}$) at time $t = 40$ with time step $\tau = 10^{-4}$
Figure 21.  Time-evolution of the volume ratio $\mathcal V_3$ for the one-dimensional linearized shallow water equations (9) with sink-like control ($B = \text{I}_{\Gamma_1}$) at time $t = 40$ with time step $\tau = 10^{-4}$
Figure 22.  Solution to two-dimensional shallow water equations (12) at time $t = 4.5$ without control ($B = 0$) by using Fourier's method (left column) and Lax-Wendroff scheme (right column) with time step $\tau = 10^{-4}$ and number of grid points $N_\xi = N_\eta = 16, 32, 64$ from top to bottom, respectively
Figure 23.  Solution to two-dimensional shallow water equations (12) at time $t = 4.5$ with distributed control ($B = \text{I}_{\mathcal U}$) by using Lax-Wendroff scheme (left column) and Fourier-Splitting (right column) with time step $\tau = 10^{-4}$ and number of grid points $N_\xi = N_\eta = 16, 32$ from top to bottom, respectively
Figure 24.  Volume ratio $\mathcal V_4$ for two-dimensional shallow water equations (12) for distributed control ($B = \text{I}_{\mathcal U}$) by using time step $\tau = 10^{-4}$
Figure 25.  Solution to two-dimensional shallow water equations (12) at time $t = 4.5$ with sink-like control ($B = \text{I}_{\Gamma_{2\ell}}$) by using Lax-Wendroff scheme (left column) and Fourier-Splitting (right column) with time step $\tau = 10^{-4}$ and number of grid points $N_\xi = N_\eta = 16, 32$ from top to bottom, respectively
Figure 26.  Volume ratio $\mathcal V_4$ for two-dimensional shallow water equations (12) for the sink-like control ($B = \text{I}_{\Gamma_1}$)
Figure 27.  Two-dimensional linearized shallow water equations with control matrix $B = \Gamma_{2r}$ representing a sink. Comparison between no control (left column), sequential splitting (column in the middle) and Strang splitting (right column)
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