TB04BX - SLICOT Library Routine Documentation

## TB04BX

### Gain of a SISO linear system, given its state-space representation, poles and zeros

[Specification] [Arguments] [Method] [References] [Comments] [Example]

Purpose

      To compute the gain of a single-input single-output linear system,
      given its state-space representation (A,b,c,d), and its poles and
      zeros. The matrix A is assumed to be in an upper Hessenberg form.
      The gain is computed using the formula

                           -1         IP              IZ
         g = (c*( S0*I - A ) *b + d)*Prod( S0 - Pi )/Prod( S0 - Zi ) ,
                                      i=1             i=1            (1)

      where Pi, i = 1 : IP, and Zj, j = 1 : IZ, are the poles and zeros,
      respectively, and S0 is a real scalar different from all poles and
      zeros.

Specification

          SUBROUTINE TB04BX( IP, IZ, A, LDA, B, C, D, PR, PI, ZR, ZI, GAIN,
         $                   IWORK )
    C     .. Scalar Arguments ..
          DOUBLE PRECISION   D, GAIN
          INTEGER            IP, IZ, LDA
    C     .. Array Arguments ..
          DOUBLE PRECISION   A(LDA,*), B(*), C(*), PI(*), PR(*), ZI(*),
         $                   ZR(*)
          INTEGER            IWORK(*)

Arguments

Input/Output Parameters

      IP      (input) INTEGER
              The number of the system poles.  IP >= 0.

      IZ      (input) INTEGER
              The number of the system zeros.  IZ >= 0.

      A       (input/output) DOUBLE PRECISION array, dimension (LDA,IP)
              On entry, the leading IP-by-IP part of this array must
              contain the state dynamics matrix A in an upper Hessenberg
              form. The elements below the second diagonal are not
              referenced.
              On exit, the leading IP-by-IP upper Hessenberg part of
              this array contains the LU factorization of the matrix
              A - S0*I, as computed by SLICOT Library routine MB02SD.

      LDA     INTEGER
              The leading dimension of array A.  LDA >= max(1,IP).

      B       (input/output) DOUBLE PRECISION array, dimension (IP)
              On entry, this array must contain the system input
              vector b.
              On exit, this array contains the solution of the linear
              system ( A - S0*I )x = b .

      C       (input) DOUBLE PRECISION array, dimension (IP)
              This array must contain the system output vector c.

      D       (input) DOUBLE PRECISION
              The variable must contain the system feedthrough scalar d.

      PR      (input) DOUBLE PRECISION array, dimension (IP)
              This array must contain the real parts of the system
              poles. Pairs of complex conjugate poles must be stored in
              consecutive memory locations.

      PI      (input) DOUBLE PRECISION array, dimension (IP)
              This array must contain the imaginary parts of the system
              poles.

      ZR      (input) DOUBLE PRECISION array, dimension (IZ)
              This array must contain the real parts of the system
              zeros. Pairs of complex conjugate zeros must be stored in
              consecutive memory locations.

      ZI      (input) DOUBLE PRECISION array, dimension (IZ)
              This array must contain the imaginary parts of the system
              zeros.

      GAIN    (output) DOUBLE PRECISION
              The gain of the linear system (A,b,c,d), given by (1).

Workspace

      IWORK   INTEGER array, dimension (IP)
              On exit, it contains the pivot indices; for 1 <= i <= IP,
              row i of the matrix A - S0*I was interchanged with
              row IWORK(i).

Method

      The routine implements the method presented in [1]. A suitable
      value of S0 is chosen based on the system poles and zeros.
      Then, the LU factorization of the upper Hessenberg, nonsingular
      matrix A - S0*I is computed and used to solve the linear system
      in (1).

References

      [1] Varga, A. and Sima, V.
          Numerically Stable Algorithm for Transfer Function Matrix
          Evaluation.
          Int. J. Control, vol. 33, nr. 6, pp. 1123-1133, 1981.

Numerical Aspects

      The algorithm is numerically stable in practice and requires
      O(IP*IP) floating point operations.

Further Comments

      None

Example

Program Text

      None

Program Data

      None

Program Results

      None

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