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<HTML>
<HEAD><TITLE>SB10ZP - SLICOT Library Routine Documentation</TITLE>
</HEAD>
<BODY>
<H2><A Name="SB10ZP">SB10ZP</A></H2>
<H3>
Transforming a SISO system into a stable and minimum phase one
</H3>
<A HREF ="#Specification"><B>[Specification]</B></A>
<A HREF ="#Arguments"><B>[Arguments]</B></A>
<A HREF ="#Method"><B>[Method]</B></A>
<A HREF ="#References"><B>[References]</B></A>
<A HREF ="#Comments"><B>[Comments]</B></A>
<A HREF ="#Example"><B>[Example]</B></A>
<P>
<B><FONT SIZE="+1">Purpose</FONT></B>
<PRE>
To transform a SISO (single-input single-output) system [A,B;C,D]
by mirroring its unstable poles and zeros in the boundary of the
stability domain, thus preserving the frequency response of the
system, but making it stable and minimum phase. Specifically, for
a continuous-time system, the positive real parts of its poles
and zeros are exchanged with their negatives. Discrete-time
systems are first converted to continuous-time systems using a
bilinear transformation, and finally converted back.
</PRE>
<A name="Specification"><B><FONT SIZE="+1">Specification</FONT></B></A>
<PRE>
SUBROUTINE SB10ZP( DISCFL, N, A, LDA, B, C, D, IWORK, DWORK,
$ LDWORK, INFO )
C .. Scalar Arguments ..
INTEGER DISCFL, INFO, LDA, LDWORK, N
C .. Array Arguments ..
INTEGER IWORK( * )
DOUBLE PRECISION A( LDA, * ), B( * ), C( * ), D( * ), DWORK( * )
</PRE>
<A name="Arguments"><B><FONT SIZE="+1">Arguments</FONT></B></A>
<P>
</PRE>
<B>Input/Output Parameters</B>
<PRE>
DISCFL (input) INTEGER
Indicates the type of the system, as follows:
= 0: continuous-time system;
= 1: discrete-time system.
N (input/output) INTEGER
On entry, the order of the original system. N >= 0.
On exit, the order of the transformed, minimal system.
A (input/output) DOUBLE PRECISION array, dimension (LDA,N)
On entry, the leading N-by-N part of this array must
contain the original system matrix A.
On exit, the leading N-by-N part of this array contains
the transformed matrix A, in an upper Hessenberg form.
LDA INTEGER
The leading dimension of the array A. LDA >= MAX(1,N).
B (input/output) DOUBLE PRECISION array, dimension (N)
On entry, this array must contain the original system
vector B.
On exit, this array contains the transformed vector B.
C (input/output) DOUBLE PRECISION array, dimension (N)
On entry, this array must contain the original system
vector C.
On exit, this array contains the transformed vector C.
The first N-1 elements are zero (for the exit value of N).
D (input/output) DOUBLE PRECISION array, dimension (1)
On entry, this array must contain the original system
scalar D.
On exit, this array contains the transformed scalar D.
</PRE>
<B>Workspace</B>
<PRE>
IWORK INTEGER array, dimension (max(2,N+1))
DWORK DOUBLE PRECISION array, dimension (LDWORK)
On exit, if INFO = 0, DWORK(1) returns the optimal value
of LDWORK.
LDWORK INTEGER
The length of the array DWORK.
LDWORK >= max(N*N + 5*N, 6*N + 1 + min(1,N)).
For optimum performance LDWORK should be larger.
</PRE>
<B>Error Indicator</B>
<PRE>
INFO INTEGER
= 0: successful exit;
< 0: if INFO = -i, the i-th argument had an illegal
value;
= 1: if the discrete --> continuous transformation cannot
be made;
= 2: if the system poles cannot be found;
= 3: if the inverse system cannot be found, i.e., D is
(close to) zero;
= 4: if the system zeros cannot be found;
= 5: if the state-space representation of the new
transfer function T(s) cannot be found;
= 6: if the continuous --> discrete transformation cannot
be made.
</PRE>
<A name="Method"><B><FONT SIZE="+1">Method</FONT></B></A>
<PRE>
First, if the system is discrete-time, it is transformed to
continuous-time using alpha = beta = 1 in the bilinear
transformation implemented in the SLICOT routine AB04MD.
Then the eigenvalues of A, i.e., the system poles, are found.
Then, the inverse of the original system is found and its poles,
i.e., the system zeros, are evaluated.
The obtained system poles Pi and zeros Zi are checked and if a
positive real part is detected, it is exchanged by -Pi or -Zi.
Then the polynomial coefficients of the transfer function
T(s) = Q(s)/P(s) are found.
The state-space representation of T(s) is then obtained.
The system matrices B, C, D are scaled so that the transformed
system has the same system gain as the original system.
If the original system is discrete-time, then the result (which is
continuous-time) is converted back to discrete-time.
</PRE>
<A name="Comments"><B><FONT SIZE="+1">Further Comments</FONT></B></A>
<PRE>
None
</PRE>
<A name="Example"><B><FONT SIZE="+1">Example</FONT></B></A>
<P>
<B>Program Text</B>
<PRE>
None
</PRE>
<B>Program Data</B>
<PRE>
None
</PRE>
<B>Program Results</B>
<PRE>
None
</PRE>
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