Rod Carvalho

Cascading systems in Haskell

January 25, 2012

Last weekend we learned how to build systems (LTI or otherwise) using Haskell. We now want to construct interconnections of systems. In this post we will study the series interconnection of systems, usually known as cascade interconnection. Parallel and feedback interconnections will be discussed in future posts.

__________

Cascading two LTI systems

Let us consider the series interconnection of two causal discrete-time LTI systems, $\mathcal{H}_1$ and $\mathcal{H}_2$ , as depicted below

where $y = \mathcal{H}_1 (x)$ and $w =\mathcal{H}_2 (y)$ are the outputs of each LTI system in the cascade. Since the output of system $\mathcal{H}_1$ is the input of system $\mathcal{H}_2$ , we have $w = (\mathcal{H}_2 \circ \mathcal{H}_1) (x)$ .

What LTI systems should we consider? Let us choose the simplest ones: the accumulator and the differentiator. Thus, let $\mathcal{H}_1$ be an accumulator (also known as “discrete-time integrator”), whose input-output relationship is as follows

$y (n) = y (n-1) + x (n)$ ,

and let $\mathcal{H}_2$ be a first difference operator (also known as “discrete-time differentiator”), whose input-output relationship is

$w (n) = y (n) - y (n-1)$ .

Note that the output of the cascade of these two LTI systems is thus

$w (n) = y (n) - y (n-1) = (y (n-1) + x (n)) - y (n-1) = x (n)$

and, hence, the cascade is input-output equivalent to the identity operator. Since $(\mathcal{H}_2 \circ \mathcal{H}_1) (x) = x$ for all signals $x$ , we say that $\mathcal{H}_2$ is the left-inverse of system $\mathcal{H}_1$ . Since both systems are LTI, the operators commute, i.e., $\mathcal{H}_2 \circ \mathcal{H}_1 = \mathcal{H}_1 \circ \mathcal{H}_2$ and, therefore, $\mathcal{H}_2$ is also the right-inverse of system $\mathcal{H}_1$ . Since $\mathcal{H}_2$ is the left- and right-inverse of $\mathcal{H}_1$ , we say that $\mathcal{H}_2$ is the inverse of system $\mathcal{H}_1$ (and vice-versa). Do keep in mind, however, that not all systems are invertible. For details, take a look at Oppenheim & Willsky [1].

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Implementation in Haskell

We can easily find a state-space realization for the accumulator, but that will not be necessary. As in previous posts, let us view discrete-signals as lists. Thus, the accumulator takes a list $[x_0, x_1, x_2, \dots]$ , and returns the following list

$[y_0, y_1, y_2, \dots] = [x_0, x_0 + x_1, x_0 + x_1 + x_2, \dots]$

where we assume that the initial condition of the accumulator is zero (i.e., $y_{-1} = 0$ ). Instead of using scanl yet once again (which would require us to drop the head of the list), let us now use scanl1 to implement the accumulator:

acc :: Num a => System a a
acc = scanl1 (+)

Please note that if the initial condition of the accumulator is not zero, we should use the following code instead:

acc' :: Num a => System a a
acc' us = tail $ scanl (+) acc_ini us

where acc_ini is the initial condition of the accumulator (analogous to the constant of integration in integral calculus).

The differentiator is not a proper system [2] and, therefore, it has no state-space realization. The differentiator takes a list $[y_0, y_1, y_2, \dots]$ , and returns the following list

$[w_0, w_1, w_2, \dots] = [y_0, y_1 - y_0, y_2 - y_1, \dots]$

where we again assume that $y_{-1} = 0$ . Note the following

$[w_0, w_1, w_2, \dots] = [y_0, y_1, y_2, \dots] - [0, y_0, y_1, \dots]$

i.e., list $w$ is obtained by elementwise subtraction of a right-shifted version of list $y$ from list $y$ itself. Subtracting two lists elementwise can be implemented using zipWith:

diff :: Num a => System a a
diff ys = zipWith (-) ys (0 : ys)

If the initial condition of the differentiator is not zero, we should instead use the following code:

diff' :: Num a => System a a
diff' ys = zipWith (-) ys (diff_ini : ys)

where diff_ini is the initial condition of the differentiator. Piecing it all together, we finally obtain the following Haskell script:

type Signal a = [a]
type System a b = Signal a -> Signal b

-- accumulator
acc :: Num a => System a a
acc = scanl1 (+)

-- differentiator
diff :: Num a => System a a
diff ys = zipWith (-) ys (0 : ys)

-- cascade of the acc. and diff.
sys :: Num a => System a a
sys = diff . acc

Take a look at the last line. It says that cascading systems is the same as composing systems! Hence, the Haskell implementation is conceptually very close to the mathematical formulation using operators. Functional analysis meets functional programming…

We run the script above on GHCi and then play with it:

*Main> -- build unit impulse
*Main> let delta = 1.0 : repeat 0.0 :: Signal Float
*Main> -- output of the accumulator
*Main> let ys = acc delta
*Main> take 20 ys
[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,
1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]
*Main> -- output of the differentiator
*Main> let ws = diff ys
*Main> take 20 ws
[1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]
*Main> -- impulse response of the cascade
*Main> let hs = sys delta
*Main> take 20 hs
[1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]

The impulse response of the accumulator is the unit step. The impulse response of the cascade is the unit impulse, as we expected. The differentiator is the inverse of the accumulator, and vice-versa.

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References

[1] Alan V. Oppenheim, Alan S. Willsky, S. Hamid Nawab, Signals & Systems, 2nd edition, Prentice-Hall, 1997.

[2] Panos Antsaklis, Anthony Michel, A Linear Systems Primer, Birkhäuser Boston, 2007.

Tags: Haskell, List Processing, LTI Systems, Signals & Systems, Systems Theory
Posted in Haskell, LTI Systems, Systems Theory | Leave a Comment »

Discrete-time LTI systems in Haskell II

January 22, 2012

Yesterday I wrote a post on how to implement discrete-time LTI systems in Haskell. The code I presented did not quite please my delicate aesthetic sensitivity, as it bundled the declaration of the signals with the declaration of the LTI system under study. This is not practical. Ideally, I would like to define the LTI system in a Haskell script, run it on the GHCi interpreter, then create the signals using the GHCi command line. Why? Because I can then compute the response of the system to various test input signals without reloading the script.

In this post, we will again consider the first-order causal discrete-time LTI system described by the difference equation

$y (n) - \alpha y (n-1) = u (n)$

where $|\alpha| < 1$ . In my previous post, I obtained a state-space representation of this LTI system. In this post, I will propose a better implementation of the given system in Haskell.

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Signal and System type synonyms

What is a discrete-time signal? We can think of a discrete-time signal as a sequence of numbers or symbols. In Haskell, we often use lists of floating-point numbers to represent real-valued discrete-time signals. More generally, we have the type synonym:

type Signal a = [a]

which says that a signal of type $a$ is a list of elements of type $a$ . We can then have signals of various types: integer, fixed-point, floating-point, bit, binary word, etc. This should be particularly useful in case we want to simulate mixed-signal circuits.

What is a discrete-time system? It is that which maps input discrete-time signals to output discrete-time signals. We will use the type synonym:

type System a b = (Signal a -> Signal b)

which tells us that a system of type $a \,\, b$ is a function that maps an input signal of type $a$ to an output signal of type $b$ . In other words, it takes a list whose elements are of type $a$ , and it returns a list whose elements are of type $b$ .

__________

Implementing the LTI system in Haskell

In my previous post, I created the state and output sequences using

xs = scanl f x0 us
ys = zipWith g xs us

Combining the two lines above into a single line, we obtain

ys = zipWith g (scanl f x0 us) us

which returns the output sequence when given the input sequence. Note that the state sequence is not created. Assuming zero initial condition, we can then create a system (i.e., a function that takes a list and returns a list) as follows

sys :: Floating a => System a a
sys us = zipWith g (scanl f 0.0 us) us

which takes signals of type $a$ and returns signals of the same type, where $a$ is a floating-point type (either Float or Double).

Putting it all together in a script, we finally obtain:

type Signal a = [a]

type System a b = (Signal a -> Signal b)

-- state-space model
f,g :: Floating a => a -> a -> a
f x u = (0.5 * x) + u  	-- state-transition
g x u = f x u		-- output

-- define LTI system
sys :: Floating a => System a a
sys us = zipWith g (scanl f 0.0 us) us

which defines the LTI system under study with $\alpha = 1/2$ . For this choice of $\alpha$ , the system is a lowpass filter.

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Example: lowpass-filtering a square wave

We run the script above on GHCi to create the desired LTI system. Let us now create an input signal, a square wave of period equal to 16 and duty cycle equal to 50%, and lowpass-filter it using the LTI system we created:

*Main> -- create square wave
*Main> let ones  = take 8 $ repeat 1.0 :: Signal Float
*Main> let zeros = take 8 $ repeat 0.0 :: Signal Float
*Main> let us = cycle (ones ++ zeros) :: Signal Float
*Main> -- create output sequence
*Main> let ys = sys us
*Main> -- check types
*Main> :type us
us :: Signal Float
*Main> :type ys
ys :: Signal Float
*Main> :type sys
sys :: Floating a => System a a

Finally, we compute the first 64 samples of the input and output signals using function take, and plot the signals using MATLAB:

In case you are wondering why the peak amplitude of the output signal is twice that of the input signal, compute the transfer function of the LTI system and note that the DC gain is equal to $\frac{1}{1 - \alpha} = 2$ .

Tags: Haskell, List Processing, LTI Systems, Signals & Systems, Systems Theory
Posted in Haskell, LTI Systems, Systems Theory | 1 Comment »

Discrete-time LTI systems in Haskell

January 21, 2012

Consider the first-order causal discrete-time LTI system described by the following difference equation

$y (n) - \alpha y (n-1) = u (n)$

where $|\alpha| < 1$ . This system can be represented by the block diagram

where $D$ is a unit-delay block, the output of which is $y (n-1)$ . We now introduce state variable $x (n)$ denoting the output of the unit-delay block, i.e., $x(n) = y(n-1)$ . Thus, $x (n+1) = y(n)$ and, since $y (n) = \alpha y (n-1) + u (n)$ , we obtain the following state-transition equation

$x (n+1) = \alpha x (n) + u (n)$ .

The initial condition is $x(0) = y(-1)$ , which we assume to be zero. Note that the output is $y (n) = x(n+1)$ and, thus, the output equation is

$y (n) = \alpha x (n) + u (n)$ .

Lastly, we define $f (x,u) = g (x,u) := \alpha x + u$ , which allows us to write the state-transition and output equations as follows

$\begin{array}{rl} x (n+1) &= f (x(n), u(n))\\ y (n) &= g (x(n), u(n))\\\end{array}$

We can now implement this state-space model in Haskell!

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Implementation in Haskell

Given an initial state $x_0$ and an input sequence $u = [u_0, u_1, \dots]$ , we can compute the state sequence using the scanl trick I wrote about last weekend. Once we have obtained the state sequence $x = [x_0, x_1, \dots]$ , the output sequence is

$y = [y_0, y_1, \dots] = [g (x_0, u_0), g (x_1, u_1), \dots]$ .

Do you recognize the pattern? We are zipping lists $x$ and $u$ using function $g$ !! We thus use (higher-order) function zipWith to compute the output sequence.

For $\alpha = 7/8$ and a constant input sequence, the following Haskell code computes the state and output trajectories:

-- constant input sequence
us :: Floating a => [a]
us = repeat 1.0

-- parameter alpha
alpha :: Floating a => a
alpha = 7/8

-- state-transition function
f :: Floating a => a -> a -> a
f x u = (alpha * x) + u

-- output function
g :: Floating a => a -> a -> a
g x u = f x u

-- initial condition
x0 :: Floating a => a
x0 = 0.0

-- state sequence
xs :: Floating a => [a]
xs = scanl f x0 us

-- output sequence
ys :: Floating a => [a]
ys = zipWith g xs us

Not quite. I am still stuck in the imperative paradigm… and need to be corrected. The code above does not compute anything. It does declare what the input, state and output sequences are. After running the script above in GHCi, we compute 30 samples of the input and output sequences using function take:

*Main> take 30 us
[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,
1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,
1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]
*Main> take 30 ys
[1.0,1.875,2.640625,3.310546875,3.896728515625,
4.409637451171875,4.858432769775391,5.251128673553467,
5.5947375893592834,5.895395390689373,6.158470966853201,
6.388662095996551,6.590079333996982,6.7663194172473595,
6.92052949009144,7.05546330383001,7.173530390851258,
7.276839091994852,7.367234205495495,7.446329929808559,
7.515538688582489,7.576096352509678,7.629084308445968,
7.675448769890222,7.716017673653944,7.751515464447201,
7.782576031391301,7.809754027467388,7.833534774033964,
7.854342927279719]

We can plot these sequences in MATLAB:

We now know how to simulate discrete-time LTI systems using Haskell. Note that our framework is based on state-space models, not on transfer functions. Hence, it should be possible to simulate discrete-time nonlinear systems in the same manner: using scanl to compute the state sequence, and zipWith to compute the output sequence. All we need is a state-space representation of the system under study.

Tags: Haskell, List Processing, LTI Systems, Signals & Systems, State-Space Models, Systems Theory
Posted in Haskell, LTI Systems, Systems Theory | Leave a Comment »

Communicating with half-words

January 16, 2012

Svetlana Boym on communicating with “half-words”:

There used to be a saying among Soviet intelligentsia—”to understand each other with half-words.” What is shared is silence, tone of voice, nuance of intonation. To say a full word is to say too much; communication on the level of words is already excessive, banal, almost kitschy. This peculiar form of communication “with half-words” is a mark of belonging to an imagined community that exists on the margin of the official public sphere. Hence the American metaphors for being sincere and authentic—”saying what you mean,” “going public,” and “being straightforward”—do not translate properly into the Soviet and Russian contexts. “Saying what you mean” could be interpreted as being stupid, naïve, or not streetwise. Such a profession of sincerity could be seen, at best, as a sign of foreign theatrical behavior; at worst, as a cunning provocation. There is no word for authenticity in Russian, but there are two words for truth, pravda and istina. It is possible to tell the truth (pravda), but istina—the word that, according to Vladimir Nabokov, does not rhyme with anything—must remain unarticulated.

In Russian cyrillic: istina = истина, pravda = правда. I would be delighted if any native Russians would care to comment on or critique this passage.

__________

Source:

Svetlana Boym, Common places: mythologies of everyday life in Russia, Harvard University Press, 1994.

Tags: Russian Culture, Svetlana Boym
Posted in Thoughts | 3 Comments »

Computing state trajectories using scans

January 15, 2012

Consider a class of dynamical systems of the form

$x_{k+1} = f (x_k, u_k )$

where $x_k$ is the state, $u_k$ is the input, and $f : \mathcal{X} \times \mathcal{U} \to \mathcal{X}$ is the state-transition function, where $\mathcal{X}$ and $\mathcal{U}$ are the state set and input set, respectively.

Given an initial state $x_0$ and a (possibly infinite) input sequence $(u_0, u_1, u_2, \dots)$ , we would like to compute the state trajectory $(x_0, x_1, x_2, x_3, \dots)$ . Iterating, we obtain

$\begin{array}{rl} x_1 &= f (x_0, u_0)\\ x_2 &= f (x_1, u_1) = f (f (x_0, u_0), u_1)\\ x_3 &= f (x_2, u_2) = f (f (x_1, u_1), u_2) = f (f (f (x_0, u_0), u_1), u_2)\end{array}$

and so on. Let us pause to take a look at the following equation

$x_3 = f (f (f (x_0, u_0), u_1), u_2)$ .

If you happen to be acquainted with functional programming, you will probably recognize this pattern. It is a left fold!! Hence, for every integer $N \geq 1$ , we can compute $x_N$ in Haskell using function foldl, as follows

$\text{foldl} \, f \, x_0\, [u_0, u_1, \dots, u_{N-1}]$ .

However, we would like to obtain the intermediate states $x_1, x_2, \dots, x_{N-1}$ as well. We can compute the entire state trajectory using function scanl instead

$\text{scanl} \, f \, x_0\, [u_0, u_1, \dots, u_{N-1}]$

which returns a (finite) list of states $[ x_0, x_1, \dots, x_N]$ . Amazingly (or perhaps not), since Haskell is lazy, we can obtain a state trajectory of infinite length corresponding to a given initial state and a given input sequence of infinite length!

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Example

Consider the logistic map with a control input

$x_{k+1} = r x_k (1 - x_k) + u_k$

where we will use $r = 3.8$ , for which the dynamics (of the unforced system) are chaotic. Let the initial state be $x_0 = 0$ (note that $x = 0$ is a fixed-point of the unforced logistic map), and let the input sequence be a discrete-time sinusoid of infinite length

$u_k = \displaystyle\frac{1}{5} \sin \left(\frac{\pi}{10} k\right)$

whose period is $20$ . The following Haskell program computes the infinite-length state trajectory using the aforementioned scanl trick:

-- define logistic map with control input
f :: Floating a => a -> a -> a
f x u = (3.8 * x * (1 - x)) + u

-- frequency of the input sinusoid
omega :: Floating a => a
omega = pi / 10

-- generate sinusoidal control input
us :: Floating a => [a]
us = map ((0.2*) . sin . (omega*) . fromIntegral) [0..]

-- initial state
x0 :: Floating a => a
x0 = 0.0

-- compute the state trajectory
xs :: Floating a => [a]
xs = scanl f x0 us

where we used map, function composition, and an infinite list of non-negative integers to generate the sinusoidal input.

To be more precise, the program above does not quite compute the state trajectory, it merely “promises” to compute it when needed. After running the program in GHCi, we can obtain the first 20 values of the input sequence and the first 21 values of the state sequence using function take, as follows

*Main> take 20 us
[0.0,6.180339887498948e-,0.11755705045849463,
0.1618033988749895,0.1902113032590307,0.2,
0.19021130325903074,0.1618033988749895,
0.11755705045849466, 6.180339887498951e-2,
2.4492127076447546e17,-6.180339887498946e2,
-0.11755705045849461,-0.16180339887498948,
-0.1902113032590307,-0.2,-0.19021130325903074,
-0.1618033988749895,-0.11755705045849468,
-6.180339887498953e-2]
*Main> take 21 xs
[0.0,0.0,6.180339887498948e-2,0.33789525775595064,
1.0119471985345525,0.14426955372699996,
0.6691322284587664,1.031509602586003,
3.8294059838691885e-2,0.2575020247735924,
0.7883433805171414,0.6340607606653986,
0.8199019084343063,0.44356147166584237,
0.7760924326990134,0.4701259774450641,
0.7466086625502713,0.5286885334506017,
0.7850690797091344,0.5236383047578965,
0.8860732772080671]

In other words, the input and state sequences are only computed when the expressions with take are evaluated by the interpreter. We can plot these sequences in MATLAB:

Last but not least, here is the MATLAB code that generates the plot:

figure; hold on;
stem([0:1:19],u,'b');
stem([0:1:20],x,'r');
legend('input','state');
xlabel('k (time)'); ylabel('amplitude');
axis([0 21 -0.25 1.20]);

where (row) vectors $u$ and $x$ are the lists produced by GHCi.

__________

Lastly, please do note that the class of dynamical systems we have considered in this post is extremely broad. It encompasses difference equations obtained from the time-discretization of differential equations, IIR filters, finite and infinite automata, etc.

Tags: Chaotic Maps, Dynamical Systems, Functional Programming, Haskell, Higher-Order Functions, List Processing, Logistic Map
Posted in Dynamical Systems, Haskell | Leave a Comment »

Rod Carvalho

Communicating with half-words

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Rod Carvalho

Cascading systems in Haskell

Discrete-time LTI systems in Haskell II

Discrete-time LTI systems in Haskell

Communicating with half-words

Computing state trajectories using scans

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