Continuous-time systems¶
Linear time invariant filters¶
Linear time invariant filters are created with the LTIFilter class:
>>> fil = LTIFilter(b, a)
- where b is a list or array of the transfer function numerator
coefficients and a is a list or array of the transfer function denominator coefficients. For example,
>>> fil = LTIFilter(('b0', ), ('a0', 'a1'))
The coefficients can be found from the a and b attributes:
>>> fil.a
(a₀, a₁)
>>> fil.b
(b₀,)
The filter’s transfer function is found with the transfer_function() method:
>>> fil.transfer_function()
b₀
─────────
a₀⋅s + a₁
its frequency response is found with the frequency_response() method:
>>> fil.frequency_response()
b₀
───────────────
2⋅ⅉ⋅π⋅a₀⋅f + a₁
its impulse response is found with the impulse_response() method:
>>> fil.impulse_response()
-a₁⋅t
──────
a₀
b₀⋅ℯ ⋅u(t)
───────────────
a₀
its step response is found with the step_response() method:
>>> fil.step_response()
⎛ -a₁⋅t ⎞
⎜ ──────⎟
⎜ a₀ ⎟
⎜1 ℯ ⎟
b₀⋅⎜── - ───────⎟⋅u(t)
⎝a₁ a₁ ⎠
The filter’s differential equation is found with the differential_equation() method:
>>> fil.differential_equation()
d
a₀⋅y(t) = - a₁⋅──(y(t)) + b₀⋅x(t)
dt
The input and output symbols can be changed with the inputsym and outputsym arguments.
The response due to intial conditions is found with the initial_response() method:
>>> fil.initial_response(('y0', ))
⎛ -a₁⋅t ⎞
⎜ ────── ⎟
⎜ a₀ ⎟
⎜δ(t) a₁⋅ℯ ⋅u(t)⎟
-a₁⋅y₀⋅⎜──── - ───────────────⎟
⎜ a₀ 2 ⎟
⎝ a₀ ⎠
Continuous-time linear time invariant filter attributes¶
a denominator coefficients as a list
b numerator coefficients as a list
is_marginally_stable True if impulse response marginally stable
is_stable True if impulse response stable
Continuous-time linear time invariant filter methods¶
differential_equation() creates discrete-time differential equation
impulse_response() creates discrete-time domain impulse response
initial_response() returns discrete time-domain response due to initial conditions
response() returns discrete time-domain response due to input signal and initial conditions
transfer_function() creates s-domain transfer function
sdomain_initial_response() returns s-domain response due to initial conditions
Differential equations¶
Differential equations are represented by the DifferentialEquation class. They are usually created by the differential_equation() method of the LTIFilter class or from transfer functions. For example:
>>> fil = LTIFilter(('b0', ), ('a0', 'a1'))
>>> de = fil.differential_equation()
>>> de
d
a₀⋅y(t) = - a₁⋅──(y(t)) + b₀⋅x(t)
dt
There are two attributes: lhs for the left-hand-side and rhs for the right-hand-side,
>>> de.lhs
a₀⋅y(t)
>>> de.rhs
d
- a₁⋅──(y(t)) + b₀⋅x(t)
dt
A transfer function is created with the transfer_function() method:
>>> de.transfer_function()
b₀
─────────
a₀ + a₁⋅s
An LTFilter object is created with the lti_filter() method:
>>> fil = de.lti_filter()
Differential equation attributes¶
lhs left-hand-side of the equation
rhs right-hand-side of the equation
inputsym input symbol, usually ‘x’
outputsym input symbol, usually ‘y’
Differential equation methods¶
dlti_filter() creates continuous-time linear time invariant filter (LTIFilter) object
separate() separates the input expressions from the output expressions.
transfer_function() creates s-domain transfer function
Continuous-time state-space representation¶
Lcapy has two state-space representations: StateSpace for continuous-time linear time-invariant systems and DTStateSpace for discrete-time linear time-invariant systems. Both representations share many methods and attributes.
A state-space object is created from the state matrix, A, input matrix, B, output matrix C, and feed-through matrix D:
>>> ss = StateSpace(A, B, C, D)
A state-space object can also be created from lists of the numerator and denominator coefficients b and a:
>>> ss = StateSpace.from_ba(b, a)
By default, the controllable canonical form CCF is created. The observable canonical form OCF is created with:
>>> ss = StateSpace.from_ba(b, a, form='OCF')
Similarly, the diagonal canonical form DCF is created with:
>>> ss = StateSpace.from_ba(b, a, form='DCF')
For the DCF, the poles of the transfer function must be unique.
State-space from transfer function¶
Transfer functions (and impedances and admittances) can be converted to a state-space representation. Here’s an example:
>>> Z = (s**2 + a) / (s**3 + b * s + c)
>>> ss = Z.state_space('CCF')
State-space representation are not unique; Lcapy uses the controllable canonical form (CCF), the observable canonical form (OCF), and the diagonal canonical form (DCF). The CCF form of the state-space matrices are:
>>> ss.A
⎡0 1 0⎤
⎢ ⎥
⎢0 0 1⎥
⎢ ⎥
⎣-c -b 0⎦
>>> ss.B
⎡0⎤
⎢ ⎥
⎢0⎥
⎢ ⎥
⎣1⎦
>>> ss.C
[a 0 1]
>>> ss.D
[0]
Transfer function from state-space¶
For a single-input single-output (SISO) system the transfer function is obtained with the transfer_function() method, for example:
>>> ss = StateSpace(A, B, C, D)
>>> G = ss.transfer_function
State-space operations¶
Model balancing¶
This returns a new StateSpace object that has the controllability and observability gramians equal to the diagonal matrix with the Hankel singular values on the diagonal. For example:
>>> ss2 = ss.balance()
Note, this requires numerical A, B, C, D matrices.
Model reduction¶
A balanced reduction can be performed using:
>>> ss2 = ss.balance_reduce(threshold=0.1)
where states are removed with a Hankel singular value below the threshold. Note, this requires numerical A, B, C, D matrices.
Alternatively, specific states can be removed. For example:
>>> ss2 = ss.reduce(elim_states=[1, 3])
Discrete-time systems¶
Difference equations¶
Difference equations can be generated from transfer functions and impulse responses. Both FIR and IIR (direct form I) can be generated. For example:
>>> H = (z + 2) / z**2
>>> H.difference_equation('x', 'y', 'fir')
y(n) = 2⋅x(n - 2) + x(n - 1)
Difference equations can be created explicitly, for example:
>>> de = difference_equation('y(n)', '2 * x(n - 2) + x(n - 1)')
The separate() method separates the input expressions from the output expressions. For example:
>>> de = difference_equation('y(n)', '2 * y(n - 1) + x(n)')
>>> de.separate()
y(n) - 2⋅y(n - 1) = x(n)
Difference equation attributes¶
lhs left-hand-side of the equation
rhs right-hand-side of the equation
inputsym input symbol, usually ‘x’
outputsym input symbol, usually ‘y’
Difference equation methods¶
dlti_filter() creates discrete-time linear time invariant filter (DLTIFilter) object
separate() separates the input expressions from the output expressions.
transfer_function() creates z-domain transfer function
Discrete-time transfer functions¶
A discrete-time transfer functions can be determined from a difference equation or a DLTI filter. For example:
>>> de = difference_equation('y(n)', '2 * x(n - 2) + x(n - 1)')
>>> H = de.transfer_function()
>>> H
z + 2
─────
2
z
Discrete-time transfer function methods¶
dlti_filter() creates discrete-time linear time invariant filter (DLTIFilter) object
difference_equation() creates discrete-time difference equation
Discrete-time linear time invariant filters¶
A discrete-time linear time invariant filter can be specified by its numerator and denominator coefficients. For example, a first-order, discrete-time, recursive low-pass filter can be created with:
>>> a = symbol('a')
>>> lpf = DLTIFilter((1 - a, ), (1, -a))
The difference equation can be printed using:
>>> lpf.difference_equation()
y(n) = a⋅y(n - 1) + (1 - a)⋅x(n)
The transfer function can be printed using:
>>> lpf.transfer_function()
z⋅(a - 1)
─────────
a - z
The impulse response can be printed using:
>>> lpf.impulse_response()
n
a ⋅(1 - a)⋅u[n]
The general response to an input x(n) can be printed using:
>>> lpf.response(x, ni=(0, 5))
For a recursive filter, the initial conditions can also be specified:
>>> lpf.response(x, ic=[1], ni=(0, 5))
The input to the filter can be a DiscreteTimeDomainExpression or a sequence. The output is a sequence.
A discrete-time LTI filter can be created from difference equations and transfer functions. For example:
>>> de = DifferenceEquation('2 * y(n)', '4 * y(n + 1) - 3 * y(n-3) -2 * x(n) - 5 * x(n-3)')
>>> fil = de.dlti_filter()
>>> fil.a
[4, -2, 0, 0, -3]
>>> fil.b
[0, 2, 0, 0, 5]
>>> fil.difference_equation()
Discrete-time linear time invariant filter attributes¶
a denominator coefficients as a list
b numerator coefficients as a list
is_marginally_stable True if impulse response marginally stable
is_stable True if impulse response stable
Discrete-time linear time invariant filter methods¶
difference_equation() creates discrete-time difference equation
impulse_response() creates discrete-time domain impulse response
initial_response() returns discrete time-domain response due to initial conditions
inverse() creates an inverse filter by switching numerator and denominator coefficients
response() returns discrete time-domain response due to input signal and initial conditions
transfer_function() creates z-domain transfer function
zdomain_initial_response() returns z-domain response due to initial conditions
Discrete-time state-space representation¶
Discrete-time state-space objects are defined in a similar manner to continuous-time state-space objects and share many methods and attributes. A discrete-time state-space object is created from the state matrix, A, input matrix, B, output matrix C, and feed-through matrix D:
>>> ss = DTStateSpace(A, B, C, D)
A state-space object can also be created from lists of the numerator and denominator coefficients b and a:
>>> ss = DTStateSpace.from_ba(b, a)
By default, the controllable canonical form CCF is created. The observable canonical form OCF is created with:
>>> ss = DTStateSpace.from_ba(b, a, form='OCF')
Similarly, the diagonal canonical form DCF is created with:
>>> ss = DTStateSpace.from_ba(b, a, form='DCF')
For the DCF, the poles of the transfer function must be unique.
For example:
>>> ss = DTStateSpace(((0, 1), (1, 0)), (1, 1), ((1, 2), ), [1])
>>> ss.A
⎡0 1⎤
⎢ ⎥
⎣1 0⎦
>>> ss.B
⎡1⎤
⎢ ⎥
⎣1⎦
>>> ss.C
[1 2]
>>> ss.D
[1]
>>> ss.state_equations()
⎡x₀(n + 1)⎤ ⎡0 1⎤ ⎡x₀(n)⎤ ⎡1⎤
⎢ ⎥ = ⎢ ⎥⋅⎢ ⎥ + ⎢ ⎥⋅[u₀(n)]
⎣x₁(n + 1)⎦ ⎣1 0⎦ ⎣x₁(n)⎦ ⎣1⎦
>>> ss.output_equations()
⎡x₀(n)⎤
[y₀(n)] = [1 2]⋅⎢ ⎥ + [1]⋅[u₀(n)]
⎣x₁(n)⎦
>>> ss.controllability_matrix
⎡1 1⎤
⎢ ⎥
⎣1 1⎦
>>> ss.is_controllable
False
>>> ss = DTStateSpace(((0, 1), (1, 1)), (1, 1), ((1, 2), ), [1])
>>> ss.A
⎡0 1⎤
⎢ ⎥
⎣1 1⎦
>>> ss.B
⎡1⎤
⎢ ⎥
⎣1⎦
>>> ss.C
[1 2]
>>> ss.D
[1]
>>> ss.is_stable
False
>>> ss.eigenvalues
[-1, 1]
>>> ss.controllability_matrix
⎡1 1⎤
⎢ ⎥
⎣1 2⎦
>>> ss.is_controllable
True
>>> ss.is_observable
True
>>> ss.state_transfer([[2], [3]], xinitial=[0, 0])
⎡5⎤
⎢ ⎥
⎣7⎦
>>> ss.minimum_energy_input(2, [5, 7], [0, 0])
⎡2⎤
⎢ ⎥
⎣3⎦
>>> ss.minimum_energy(2, [5, 7], [0, 0])
>>> ss.minimum_energy_input(3, [5, 7], [0, 0])
⎡5/3⎤
⎢ ⎥
⎢1/3⎥
⎢ ⎥
⎣4/3⎦
>>> ss.minimum_energy(3, [5, 7], [0, 0])
14/3