# State Space Filters

An important representation for discrete-time linear systems is the
*state-space* formulation

where is the length

*state vector*at discrete time , is a vector of inputs, and the output vector. is the

*state transition matrix*,

^{G.1}and it determines the

*dynamics*of the system (its

*poles*or resonant

*modes*).

The state-space representation is especially powerful for
*multi-input, multi-output* (MIMO) linear systems, and also for
*time-varying* linear systems (in which case any or all of the matrices
in Eq.(G.1) may have time subscripts ) [37].
State-space models are also used extensively in the field of
*control systems* [28].

An example of a Single-Input, Single-Ouput (SISO) state-space model appears in §F.6.

## Markov Parameters

The *impulse response* of a state-space model is easily found by direct
calculation using Eq.(G.1):

Note that we have assumed
(*zero initial state* or
*zero initial conditions*). The notation
denotes a
matrix having along the diagonal and zeros
elsewhere.^{G.2}

The impulse response of the state-space model can be summarized as

(G.2) |

The impulse response terms for are known as the
*Markov parameters* of the state-space model.

Note that each sample of the impulse response
is a matrix.^{G.3} Therefore, it is not
a possible output signal, except when . A better name might be
``impulse-matrix response''. In
§G.4 below, we'll see that
is the inverse *z* transform of the
matrix transfer-function of the system.

Given an arbitrary input signal
(and zero intial conditions
), the output signal is given by the *convolution* of the
input signal with the impulse response:

## Response from Initial Conditions

The response of a state-space model to *initial conditions*, *i.e.*,
its *initial state*
is given by, again using Eq.(G.1),

## Complete Response

The *complete response* of a linear system consists of the
superposition of (1) its response to the input signal
and (2)
its response to initial conditions
:

## Transfer Function of a State Space Filter

The *transfer function* can be defined as the transform of
the impulse response:

^{G.4}we obtain

Note that if there are inputs and outputs, is a

*transfer-function matrix*(or ``matrix transfer function'').

### Example State Space Filter Transfer Function

In this example, we consider a second-order filter () with two inputs () and two outputs ():

so that

From Eq.(G.5), the transfer function of this MIMO digital filter is then

Note that when , the state transition matrix is simply a 2D
rotation matrix, rotating through the angle for which
and
. For , we have a type of
*normalized second-order resonator* [51],
and controls the ``damping'' of the resonator, while
controls the resonance frequency . The resonator
is ``normalized'' in the sense that the filter's state has a constant
norm (``preserves energy'') when and the input is zero:

since a rotation does not change the norm, as can be readily checked.

In this two-input, two-output digital filter, the input drives state while input drives state . Similarly, output is , while is . The two-by-two transfer-function matrix contains entries for each combination of input and output. Note that all component transfer functions have the same poles. This is a general property of physical linear systems driven and observed at arbitrary points: the resonant modes (poles) are always the same, but the zeros vary as the input or output location are changed. If a pole is not visible using a particular input/output pair, we say that the pole has been ``canceled'' by a zero associated with that input/output pair. In control-theory terms, the pole is ``uncontrollable'' from that input, or ``unobservable'' from that output, or both.

## Transposition of a State Space Filter

Above, we found the transfer function of the general state-space model to be

*transpose*of this equation gives

*transpose*of the system . The transpose is obtained by interchanging and in addition to transposing all matrices.

When there is only one input and output signal (the SISO case), is a scalar, as is . In this case we have

*same*as the untransposed system in the scalar case. It can be shown that transposing the state-space representation is equivalent to

*transposing the signal flow graph*of the filter [75]. The equivalence of a flow graph to its transpose is established by

*Mason's gain theorem*[49,50]. See §9.1.3 for more on this topic.

## Poles of a State Space Filter

In this section, we show that the *poles of a state-space model* are given
by the *eigenvalues* of the state-transition matrix .

Beginning again with the transfer function of the general state-space model,

*controllable*and

*observable*[37].) By Cramer's rule for matrix inversion, the denominator polynomial for is given by the

*determinant*

*determinant*of the square matrix . (The determinant of is also often written .) In linear algebra, the polynomial is called the

*characteristic polynomial*for the matrix . The roots of the characteristic polynomial are called the

*eigenvalues*of .

Thus, *the eigenvalues of the state transition matrix are the
poles of the corresponding linear time-invariant system*. In
particular, note that the poles of the system do not depend on the
matrices , although these matrices, by placing system zeros,
can cause pole-zero cancellations (unobservable or uncontrollable
modes).

## Difference Equations to State Space

Any explicit LTI difference equation (§5.1) can be converted
to state-space form. In state-space form, many properties of the
system are readily obtained. For example, using standard utilities
(such as in Matlab), there are functions for computing the
*modes* of the system (its poles), an equivalent
*transfer-function* description, *stability* information,
and whether or not modes are ``observable'' and/or ``controllable''
from any given input/output point.

Every th order scalar (ordinary) difference equation may be reformulated
as a *first order* *vector* difference equation. For example,
consider the second-order difference equation

We may define a vector first-order difference equation--the ``state space representation''--as discussed in the following sections.

### Converting to State-Space Form by Hand

Converting a digital filter to state-space form is easy because there are various ``canonical forms'' for state-space models which can be written by inspection given the strictly proper transfer-function coefficients.

The canonical forms useful for transfer-function to state-space
conversion are
*controller canonical form* (also called *control* or
*controllable canonical form*) and *observer canonical form*
(or *observable canonical form*) [28, p.
80], [37]. These names come from the
field of *control theory* [28] which is
concerned with designing feedback laws to control the dynamics of
real-world physical systems. State-space models are used extensively
in the control field to model physical systems.

The name ``controller canonical form'' reflects the fact that the
input signal can ``drive'' all modes (poles) of the system. In the
language of control theory, we may say that all of the system poles
are *controllable* from the input
. In observer canonical form, all modes are guaranteed to be
*observable*. Controllability and observability of a state-space
model are discussed further in §G.7.3 below.

The following procedure converts any causal LTI digital filter into state-space form:

- Determine the filter transfer function .
- If is not strictly proper (), ``pull out'' the delay-free path to obtain a feed-through gain in parallel with a strictly proper transfer function.
- Write down the state-space representation by inspection using
controller canonical form for the strictly proper transfer function.
(Or use the matlab function
`tf2ss`.)

We now elaborate on these steps for the general case:

- The general causal IIR filter

has transfer function

- By convention, state-space descriptions handle any delay-free
path from input to output via the direct-path coefficient in
Eq.(G.1). This is natural because the delay-free path does not
affect the state of the system.
A causal filter contains a delay-free path if its impulse response is nonzero at time zero,

*i.e.*, if .^{G.5}In such cases, we must ``pull out'' the delay-free path in order to implement it in parallel, setting in the state-space model.In our example, one step of long division yields

where , with for , and for . - The controller canonical form is then easily written as follows:

An alternate controller canonical form is obtained by applying the similarity transformation (see §G.8 below) which simply reverses the order of the state variables. Any permutation of the state variables would similarly yield a controllable form. The transpose of a controllable form is an observable form.

One might worry that choosing controller canonical form may result in unobservable modes. However, this will not happen if and have no common factors. In other words, if there are no pole-zero cancellations in the transfer function , then either controller or observer canonical form will yield a controllable and observable state-space model.

We now illustrate these steps using the example of Eq.(G.7):

- The transfer function can be written, by inspection, as

- We need to convert Eq.(G.13) to the form

Obtaining a common denominator and equating numerator coefficients with Eq.(G.13) yields

The same result is obtained using long division (or synthetic division). - Finally, the controller canonical form is given by

### Converting Signal Flow Graphs to State-Space Form by Hand

The procedure of the previous section quickly converts any*transfer function*to state-space form (specifically, controller canonical form). When the starting point is instead a signal flow graph, it is usually easier to go directly to state-space form by

*labeling each delay-element output as a state variable*and writing out the state-space equations by inspection of the flow graph.

For the example of the previous section, suppose we are given
Eq.(G.14) in *direct-form II* (DF-II), as shown in
Fig.G.1. It is important that the filter representation be
*canonical with respect to delay*, *i.e.*, that the number of
delay elements equals the order of the filter. Then the third step
(writing down controller canonical form by inspection) may replaced by the
following more general procedure:

- Assign a state variable to the output of each delay element (indicated in Fig.G.1).
- Write down the state-space representation by inspection of the flow graph.

The state-space description of the difference equation in
Eq.(G.7) is given by Eq.(G.16).
We see that controller canonical form follows immediately from the
direct-form-II digital filter realization, which is fundamentally an
all-pole filter followed by an all-zero (FIR) filter (see
§9.1.2). By starting instead from the
*transposed direct-form-II* (TDF-II) structure, the
*observer canonical form* is obtained [28, p.
87]. This is because the zeros effectively precede the
poles in a TDF-II realization, so that they may introduce nulls in the
input spectrum, but they cannot cancel output from the poles (*e.g.*,
from initial conditions). Since the other two digital-filter direct
forms (DF-I and TDF-I--see Chapter 9 for details) are not canonical
with respect to delay, they are not used as a basis for deriving
state-space models.

### Controllability and Observability

Since the output in Fig.G.1 is a linear combination of
the input and states , one or more poles can be
*canceled* by the zeros induced by this linear combination. When that
happens, the canceled modes are said to be
*unobservable*. Of course, since we
started with a transfer function, any pole-zero cancellations should
be dealt with at that point, so that the state space realization will
always be *controllable and observable*. If a mode is
uncontrollable, the input cannot affect it; if it is unobservable, it
has no effect on the output. Therefore, there is usually no reason to
include unobservable or uncontrollable modes in a state-space
model.^{G.6}

A physical example of uncontrollable and unobservable modes is
provided by the plucked vibrating string of an *electric guitar*
with one (very thin) magnetic pick-up. In a vibrating string,
considering only one plane of vibration, each
quasi-harmonic^{G.7} overtone corresponds to a *mode
of vibration* [86] which may be modeled by a pair of
complex-conjugate poles in a digital filter which models a particular
point-to-point transfer function of the string.
All modes of vibration having a *node* at the plucking point are
*uncontrollable* at that point, and all modes having a node at
the pick-up are
*unobservable* at that point. If an ideal string is plucked at
its midpoint, for example, all even numbered harmonics will not be
excited, because they all have vibrational nodes at the string
midpoint. Similarly, if the pick-up is located one-fourth of the
string length from the bridge, every fourth string harmonic will be
``nulled'' in the output. This is why plucked and struck strings are
generally excited near one end, and why magnetic pick-ups are located
near the end of the string.

A basic result in control theory is that a system in state-space form
is *controllable* from a scalar input signal if and only
if the matrix

*i.e.*, is invertible). Here, is . For the general case, this test can be applied to each of the columns of , thereby testing controllability from each input in turn. Similarly, a state-space system is

*observable*from a given output if and only if

*i.e.*, invertible), where is . In the -output case, can be considered the row corresponding to the output for which observability is being checked.

### A Short-Cut to Controller Canonical Form

When converting a transfer function to state-space form by hand, the step of pulling out the direct path, like we did in going from Eq.(G.13) to Eq.(G.14), can be bypassed [28, p. 87].

Figure G.2 gives the standard direct-form-II structure for a second-order IIR filter. Unlike Fig.G.1, it includes a direct path from the input to the output. The filter coefficients are all given directly by the transfer function, Eq.(G.13).

This form can be converted directly to state-space form by carefully observing all paths from the input and state variables to the output. For example, reaches the output through gain 2 on the right, but also via gain on the left and above. Therefore, its contribution to the output is , as obtained in the DF-II realization with direct-path pulled out shown in Fig.G.1. The state variable reaches the output with gain , again as we obtained before. Finally, it must also be observed that the gain of the direct path from input to output is .

### Matlab Direct-Form to State-Space Conversion

Matlab and Octave support state-space models with functions such as

`tf2ss`- transfer-function to state-space conversion`ss2tf`- state-space to transfer-function conversion

Let's repeat the previous example using Matlab:

>> num = [1 2 3]; % transfer function numerator >> den = [1 1/2 1/3]; % denominator coefficients >> [A,B,C,D] = tf2ss(num,den) A = -0.5000 -0.3333 1.0000 0 B = 1 0 C = 1.5000 2.6667 D = 1 >> [N,D] = ss2tf(A,B,C,D) N = 1.0000 2.0000 3.0000 D = 1.0000 0.5000 0.3333

The `tf2ss` and `ss2tf` functions are documented online
at The Mathworks *help desk*
as well as within Matlab itself (say `help tf2ss`). In Octave,
say `help tf2ss` or `help -i tf2ss`.

### State Space Simulation in Matlab

Since matlab has first-class support for matrices and vectors, it is quite
simple to implement a state-space model in Matlab using no support functions
whatsoever, *e.g.*,

% Define the state-space system parameters: A = [0 1; -1 0]; % State transition matrix B = [0; 1]; C = [0 1]; D = 0; % Input, output, feed-around % Set up the input signal, initial conditions, etc. x0 = [0;0]; % Initial state (N=2) Ns = 10; % Number of sample times to simulate u = [1, zeros(1,Ns-1)]; % Input signal (an impulse at time 0) y = zeros(Ns,1); % Preallocate output signal for n=0:Ns-1 % Perform the system simulation: x = x0; % Set initial state for n=1:Ns-1 % Iterate through time y(n) = C*x + D*u(n); % Output for time n-1 x = A*x + B*u(n); % State transitions to time n end y' % print the output y (transposed) % ans = % 0 1 0 -1 0 1 0 -1 0 0The restriction to indexes beginning at 1 is unwieldy here, because we want to include time in the input and output. It can be readily checked that the above examples has the transfer function

`filter`function:

NUM = [0 1]; DEN = [1 0 1]; y = filter(NUM,DEN,u) % y = % 0 1 0 -1 0 1 0 -1 0 1To eliminate the unit-sample delay,

*i.e.*, to simulate in state-space form, it is necessary to use the (feed-around) coefficient:

[A,B,C,D] = tf2ss([1 0 0], [1 0 1]) % A = % 0 1 % -1 -0 % % B = % 0 % 1 % % C = % -1 0 % % D = 1 x = x0; % Reset to initial state for n=1:Ns-1 y(n) = C*x + D*u(n); x = A*x + B*u(n); end y' % ans = % 1 0 -1 0 1 0 -1 0 1 0Note the use of trailing zeros in the first argument of

`tf2ss`(the transfer-function numerator-polynomial coefficients) to make it the same length as the second argument (denominator coefficients). This is

*necessary*in

`tf2ss`because the same function is used for both the continous- and discrete-time cases. Without the trailing zeros, the numerator will be extended by zeros on the

*left*,

*i.e.*, ``right-justified'' relative to the denominator.

### Other Relevant Matlab Functions

Related Signal Processing Toolbox functions include

`tf2sos`-- Convert digital filter transfer function parameters to second-order sections form. (See §9.2.)`sos2ss`-- Convert second-order filter sections to state-space form.^{G.8}`tf2zp`-- Convert transfer-function filter parameters to zero-pole-gain form.`ss2zp`-- Convert state-space model to zeros, poles, and a gain.`zp2ss`-- Convert zero-pole-gain filter parameters to state-space form.

In Matlab, say `lookfor state-space` to find your state-space
support utilities (there are many more than listed above). In Octave,
say `help -i ss2tf` and keep reading for more functions (the
above list is complete, as of this writing).

### Matlab State-Space Filter Conversion Example

Here is the example of §F.6 repeated using
matlab.^{G.9} The
difference equation

NUM = [0 1 1 0 ]; % NUM and DEN should be same length DEN = [1 -0.5 0.1 -0.01];The

`tf2ss`function converts from ``transfer-function'' form to state-space form:

[A,B,C,D] = tf2ss(NUM,DEN) A = 0.00000 1.00000 0.00000 0.00000 0.00000 1.00000 0.01000 -0.10000 0.50000 B = 0 0 1 C = 0 1 1 D = 0

## Similarity Transformations

A *similarity transformation* is a *linear change of coordinates*.
That is, the original -dimensional state vector
is recast
in terms of a new coordinate basis. For any *linear
transformation* of the coordinate basis, the transformed state vector
may be computed by means of a matrix multiply. Denoting the
matrix of the desired one-to-one linear transformation by , we
can express the change of coordinates as

Let's now apply the linear transformation to the general
-dimensional state-space description in Eq.(G.1). Substituting
in Eq.(G.1) gives

(G.17) |

Premultiplying the first equation above by , we have

(G.18) |

Defining

we can write

(G.20) |

The transformed system describes the same system as in Eq.(G.1) relative to new state-variable coordinates. To verify that it's really the same system, from an input/output point of view, let's look at the transfer function using Eq.(G.5):

Since the eigenvalues of are the poles of the system, it follows that the eigenvalues of are the same. In other words, eigenvalues are unaffected by a similarity transformation. We can easily show this directly: Let denote an eigenvector of . Then by definition , where is the eigenvalue corresponding to . Define as the transformed eigenvector. Then we have

The transformed Markov parameters, , are obviously the same also since they are given by the inverse transform of the transfer function . However, it is also easy to show this by direct calculation:

## Modal Representation

When the state transition matrix is *diagonal*, we have the
so-called *modal representation*. In the single-input,
single-output (SISO) case, the general diagonal system looks like

Since the state transition matrix is diagonal, the modes are

*decoupled*, and we can write each mode's time-update independently:

Thus, the diagonalized state-space system consists of
*parallel one-pole systems*. See §9.2.2
and §6.8.7 regarding the conversion of direct-form filter
transfer functions to parallel (complex) one-pole form.

### Diagonalizing a State-Space Model

To obtain the *modal representation*, we may *diagonalize*
any state-space representation. This is accomplished by means of a
particular *similarity transformation* specified by the
*eigenvectors* of the state transition matrix . An *eigenvector*
of the square matrix is any vector
for which

*diagonalized*, as we will see below.

A system can be diagonalized whenever the eigenvectors of are
*linearly independent*. This always holds when the system
poles are *distinct*. It may or may not hold when poles are
*repeated*.

To see how this works, suppose we are able to find linearly
independent eigenvectors of , denoted
,
.
Then we can form an matrix having these eigenvectors
as columns. Since the eigenvectors are linearly independent, is
full rank and can be used as a one-to-one linear transformation, or
*change-of-coordinates* matrix. From Eq.(G.19), we have that
the transformed state transition matrix is given by

The transfer function is now, from Eq.(G.5), in the SISO case,

We have incidentally shown that the eigenvalues of the state-transition matrix are the poles of the system transfer function. When it is

*diagonal*,

*i.e.*, when diag, the state-space model may be called a

*modal representation*of the system, because the poles appear explicitly along the diagonal of and the system's dynamic modes are decoupled.

Notice that the diagonalized state-space form is essentially
equivalent to a *partial-fraction expansion* form (§6.8).
In particular, the *residue* of the th pole is given by . When complex-conjugate poles are combined to form real,
second-order blocks (in which case is block-diagonal with
blocks along the diagonal), this is
corresponds to a partial-fraction expansion into real, second-order,
parallel filter sections.

### Finding the Eigenvalues of A in Practice

Small problems may be solved by hand by solving the system of equations

`eig()`may be used to find the eigenvalues of (system poles) numerically.

^{G.10}

### Example of State-Space Diagonalization

For the example of Eq.(G.7), we obtain the following results:

>> % Initial state space filter from example above: >> A = [-1/2, -1/3; 1, 0]; % state transition matrix >> B = [1; 0]; >> C = [2-1/2, 3-1/3]; >> D = 1; >> >> eig(A) % find eigenvalues of state transition matrix A ans = -0.2500 + 0.5204i -0.2500 - 0.5204i >> roots(den) % find poles of transfer function H(z) ans = -0.2500 + 0.5204i -0.2500 - 0.5204i >> abs(roots(den)) % check stability while we're here ans = 0.5774 0.5774 % The system is stable since each pole has magnitude < 1.

Our second-order example is already in *real* form,
because it is only second order. However, to illustrate the
computations, let's obtain the eigenvectors and compute the
*complex* modal representation:

>> [E,L] = eig(A) % [Evects,Evals] = eig(A) E = -0.4507 - 0.2165i -0.4507 + 0.2165i 0 + 0.8660i 0 - 0.8660i L = -0.2500 + 0.5204i 0 0 -0.2500 - 0.5204i >> A * E - E * L % should be zero (A * evect = eval * evect) ans = 1.0e-016 * 0 + 0.2776i 0 - 0.2776i 0 0 % Now form the complete diagonalized state-space model (complex): >> Ei = inv(E); % matrix inverse >> Ab = Ei*A*E % new state transition matrix (diagonal) Ab = -0.2500 + 0.5204i 0.0000 + 0.0000i -0.0000 -0.2500 - 0.5204i >> Bb = Ei*B % vector routing input signal to internal modes Bb = -1.1094 -1.1094 >> Cb = C*E % vector taking mode linear combination to output Cb = -0.6760 + 1.9846i -0.6760 - 1.9846i >> Db = D % feed-through term unchanged Db = 1 % Verify that we still have the same transfer function: >> [numb,denb] = ss2tf(Ab,Bb,Cb,Db) numb = 1.0000 2.0000 + 0.0000i 3.0000 + 0.0000i denb = 1.0000 0.5000 - 0.0000i 0.3333 >> num = [1, 2, 3]; % original numerator >> norm(num-numb) ans = 1.5543e-015 >> den = [1, 1/2, 1/3]; % original denominator >> norm(den-denb) ans = 1.3597e-016

### Properties of the Modal Representation

The vector
in a modal representation (Eq.(G.21)) specifies how
the modes are *driven* by the input. That is, the th mode
receives the input signal weighted by
. In a computational
model of a drum, for example,
may be changed corresponding to
different striking locations on the drumhead.

The vector
in a modal representation (Eq.(G.21)) specifies how
the modes are to be *mixed* into the output. In other words,
specifies how the output signal is to be created as a
*linear combination* of the mode states:

The modal representation is not *unique* since
and
may be scaled in compensating ways to produce the same transfer
function. (The diagonal elements of may also be permuted along
with
and
.) Each element of the state vector
holds the state of a single first-order mode of the system.

For oscillatory systems, the diagonalized state transition matrix must
contain *complex* elements. In particular, if mode is both
oscillatory and *undamped* (lossless), then an excited
state-variable
will oscillate *sinusoidally*,
after the input becomes zero, at some frequency , where

In practice, we often prefer to combine complex-conjugate pole-pairs to form a real, ``block-diagonal'' system; in this case, the transition matrix is block-diagonal with two-by-two real matrices along its diagonal of the form

*complex*multiplies. The function

`cdf2rdf()`in the Matlab Control Toolbox can be used to convert complex diagonal form to real block-diagonal form.

## Repeated Poles

The above summary of state-space diagonalization works as stated when
the modes (poles) of the system are distinct. When there are two or
more resonant modes corresponding to the same ``natural frequency''
(eigenvalue of ), then there are two further subcases: If the
eigenvectors corresponding to the repeated eigenvalue (pole) are
*linearly independent*, then the modes are independent and can be
treated as distinct (the system can be diagonalized). Otherwise, we
say the equal modes are *coupled*.

The coupled-repeated-poles situation is detected when the matrix of
eigenvectors `V` returned by the
`eig` matlab function [*e.g.*, by saying
`[V,D] = eig(A)`] turns out to be *singular*.
Singularity of `V` can be defined as when its *condition
number* [`cond(V)`] exceeds some threshold, such as
`1E7`. In this case, the linearly dependent eigenvectors can
be replaced by so-called *generalized eigenvectors* [58].
Use of that similarity transformation then produces a ``block
diagonalized'' system instead of a diagonalized system, and one of the
blocks along the diagonal will be a matrix corresponding
to the pole repeated times.

Connecting with the discussion regarding repeated poles in
§6.8.5, the Jordan block corresponding to a pole
repeated times plays exactly the same role of repeated poles
encountered in a partial-fraction expansion, giving rise to terms in
the impulse response proportional to
,
, and so
on, up to
, where denotes the repeated pole
itself (*i.e.*, the repeated eigenvalue of the state-transition matrix
).

### Jordan Canonical Form

The *block diagonal* system having the eigenvalues along the
diagonal and ones in some of the superdiagonal elements (which serve
to couple repeated eigenvalues) is called *Jordan canonical
form*. Each block size corresponds to the multiplicity of the repeated
pole. As an example, a pole of multiplicity could give
rise to the following *Jordan block*:

^{G.11}Note, however, that a pole of multiplicity three can also yield two Jordan blocks, such as

Interestingly, neither Matlab nor Octave seem to have a numerical
function for computing the Jordan canonical form of a matrix. Matlab
will try to do it *symbolically* when the matrix entries are
given as exact rational numbers (ratios of integers) by the
`jordan` function, which requires the Maple symbolic
mathematics toolbox. Numerically, it is generally difficult to
distinguish between poles that are repeated exactly, and poles that
are merely close together. The `residuez` function sets a
numerical threshold below which poles are treated as repeated.

##
State-Space Analysis Example:

The Digital Waveguide Oscillator

As an example of state-space analysis, we will use it to determine the frequency of oscillation of the system of Fig.G.3 [90].

Note the assignments of unit-delay outputs to state variables and . From the diagram, we see that

A basic fact from linear algebra is that the *determinant* of a
matrix is equal to the *product of its eigenvalues*. As a quick
check, we find that the determinant of is

Note that . If we diagonalize this system to obtain , where diag, and is the matrix of eigenvectors of , then we have

If this system is to generate a real sampled sinusoid at radian frequency , the eigenvalues and must be of the form

(in either order) where is real, and denotes the sampling interval in seconds.

Thus, we can determine the frequency of oscillation (and verify that the system actually oscillates) by determining the eigenvalues of . Note that, as a prerequisite, it will also be necessary to find two linearly independent eigenvectors of (columns of ).

### Finding the Eigenstructure of A

Starting with the defining equation for an eigenvector and its corresponding eigenvalue ,

We normalized the first element of to 1 since is an eigenvector whenever is. (If there is a missing solution because its first element happens to be zero, we can repeat the analysis normalizing the second element to 1 instead.)

Equation (G.23) gives us two equations in two unknowns:

Substituting the first into the second to eliminate , we get

Thus, we have found both eigenvectors

They are linearly independent provided and finite provided .

We can now use Eq.(G.24) to find the eigenvalues:

and so this is the range of corresponding to sinusoidal oscillation. For , the eigenvalues are real, corresponding to exponential growth and decay. The values yield a repeated root (dc or oscillation).

Let us henceforth assume . In this range is real, and we have , . Thus, the eigenvalues can be expressed as follows:

Equating to , we obtain , or , where denotes the sampling rate. Thus the relationship between the coefficient in the digital waveguide oscillator and the frequency of sinusoidal oscillation is expressed succinctly as

We have now shown that the system of Fig.G.3 oscillates sinusoidally at any desired digital frequency rad/sec by simply setting , where denotes the sampling interval.

### Choice of Output Signal and Initial Conditions

Recalling that , the output signal from any diagonal state-space model is a linear combination of the modal signals. The two immediate outputs and in Fig.G.3 are given in terms of the modal signals and as

The output signal from the first state variable is

The *initial condition*
corresponds to modal initial
state

*i.e.*, it is in

*phase quadrature*with respect to ). Phase-quadrature outputs are often useful in practice,

*e.g.*, for generating complex sinusoids.

## References

Further details on state-space analysis of linear systems may be found in [102,37]. More Matlab exercises and some supporting theory may be found in [10, Chapter 5].

## State Space Problems

See `http://ccrma.stanford.edu/~jos/filtersp/State_Space_Problems.html`

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