Stabilizing Large Control Linear Systems on Multicomputers

In this paper we present several parallel algorithms for solving the stabilization problem of control linear systems. The first stabilizing algorithm, based on Bass' method, consists of matrix computations which result difficult to parallelize. A different two-stage approach, based on highly parallel spectral division techniques, is then described and used to develop parallel algorithms for the stabilization of large linear systems. The new approach consists of two well-defined stages. First, an efficient spectral division technique is used to identify the stable part of the linear system. Then, the unstable part of the system is stabilized by means of Bass' algorithm. The experimental results on a multicomputer show considerable performance improvements of these two-stage approaches over Bass' algorithm.

[1]  Robert A. van de Geijn,et al.  Parallelizing the QR Algorithm for the Unsymmetric Algebraic Eigenvalue Problem: Myths and Reality , 1996, SIAM J. Sci. Comput..

[2]  S. Godunov,et al.  Circular dichotomy of the spectrum of a matrix , 1988 .

[3]  Michael T. Heath,et al.  Parallel solution of triangular systems on distributed-memory multiprocessors , 1988 .

[4]  G. W. Stewart,et al.  A parallel implementation of the QR-algorithm , 1987, Parallel Comput..

[5]  Charles L. Lawson,et al.  Basic Linear Algebra Subprograms for Fortran Usage , 1979, TOMS.

[6]  Jack J. Dongarra,et al.  An extended set of FORTRAN basic linear algebra subprograms , 1988, TOMS.

[7]  R. Byers Solving the algebraic Riccati equation with the matrix sign function , 1987 .

[8]  E. S. Quintana,et al.  Parallel Algorithms for Solving the Algebraic Riccati Equation via the Matrix Sign Function , 1995 .

[9]  A. Laub,et al.  Rational iterative methods for the matrix sign function , 1991 .

[10]  Jack Dongarra,et al.  Level 3 BLAS for distributed memory concurrent computers , 1993 .

[11]  V. Hernandez,et al.  Solving linear matrix equations in control problems on distributed memory multiprocessors , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[12]  JUDITH D. GARDINER,et al.  A Stabilized Matrix Sign Function Algorithm for Solving Algebraic Riccati Equations , 1997, SIAM J. Sci. Comput..

[13]  Z. Kovarik Some Iterative Methods for Improving Orthonormality , 1970 .

[14]  L. Balzer Accelerated convergence of the matrix sign function method of solving Lyapunov, Riccati and other matrix equations , 1980 .

[15]  Enrique S. Quintana-Ortí,et al.  Solving Discrete-Time Lyapunov Equations for the Cholesky Factor on a Shared Memory Multiprocessor , 1996, Parallel Process. Lett..

[16]  Thomas F. Coleman,et al.  A parallel triangular solver for distributed-memory multiprocessor , 1988 .

[17]  Christian H. Bischof,et al.  A BLAS-3 Version of the QR Factorization with Column Pivoting , 1998, SIAM J. Sci. Comput..

[18]  E. Armstrong,et al.  An extension of Bass' algorithm for stabilizing linear continuous constant systems , 1975 .

[19]  James Demmel,et al.  Design of a Parallel Nonsymmetric Eigenroutine Toolbox, Part I , 1993, PPSC.

[20]  Thomas F. Coleman,et al.  A New Method for Solving Triangular Systems on Distributed Memory Message-Passing Multiprocessors , 1989 .

[21]  G. Golub,et al.  A Hessenberg-Schur method for the problem AX + XB= C , 1979 .

[22]  Alan J. Laub,et al.  A Parallel Algorithm for the Matrix Sign Function , 1990, Int. J. High Speed Comput..

[23]  V. Mehrmann The Autonomous Linear Quadratic Control Problem , 1991 .

[24]  Alan J. Laub,et al.  On Scaling Newton's Method for Polar Decomposition and the Matrix Sign Function , 1990 .

[25]  Robert A. van de Geijn,et al.  LAPACK Working Note 79: Parallelizing the Q R Algorithm for the Unsymmetric Algebraic Eigenvalue Problem: Myths and Reality , 1994 .

[26]  N. Higham,et al.  Stability of methods for matrix inversion , 1992 .

[27]  Richard H. Bartels,et al.  Algorithm 432 [C2]: Solution of the matrix equation AX + XB = C [F4] , 1972, Commun. ACM.

[28]  S. Hammarling Numerical Solution of the Stable, Non-negative Definite Lyapunov Equation , 1982 .

[29]  G. Golub,et al.  Linear least squares solutions by householder transformations , 1965 .

[30]  B. Anderson,et al.  Linear Optimal Control , 1971 .

[31]  Alan J. Laub,et al.  A Newton-squaring algorithm for computing the negative invariant subspace of a matrix , 1993, IEEE Trans. Autom. Control..

[32]  Vasile Sima,et al.  An efficient Schur method to solve the stabilizing problem , 1981 .

[33]  S. Godunov Problem of the dichotomy of the spectrum of a matrix , 1986 .

[34]  J. D. Roberts,et al.  Linear model reduction and solution of the algebraic Riccati equation by use of the sign function , 1980 .

[35]  A. Malyshev Parallel Algorithm for Solving Some Spectral Problems of Linear Algebra , 1993 .

[36]  Jack Dongarra,et al.  PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing , 1995 .

[37]  Gene H. Golub,et al.  Matrix computations , 1983 .

[38]  R. B. Leipnik,et al.  Rapidly convergent recursive solution of quadratic operator equations , 1971 .

[39]  Mihail M. Konstantinov,et al.  Computational methods for linear control systems , 1991 .

[40]  J. Demmel,et al.  An inverse free parallel spectral divide and conquer algorithm for nonsymmetric eigenproblems , 1997 .

[41]  A. Laub A schur method for solving algebraic Riccati equations , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[42]  D. Kleinman,et al.  An easy way to stabilize a linear constant system , 1970 .