synapsys.core — LTI Math Engine

The mathematical core of Synapsys. All LTI system representations live here.

LTIModel (Abstract Base Class)

Base class for all LTI systems. Defines the common interface.

Method	Description
poles()	Returns the system poles as a NumPy array
zeros()	Returns the system zeros as a NumPy array
is_stable()	True if all poles satisfy the stability criterion
evaluate(s_or_z)	Evaluates the transfer function at a given complex point
step(t=None, n=200)	Step response — n applies to discrete systems only
bode(omega=None)	Returns (omega [rad/s], mag [dB], phase [deg])
simulate(t, u)	Batch simulation for an arbitrary input array

TransferFunction

Represents a SISO LTI system as a ratio of polynomials: $G(s) = N(s)/D(s)$.

Attribute / Method	Description
num	Numerator coefficients (NumPy array, highest power first)
den	Denominator coefficients (NumPy array, highest power first)
dt	Sample time (0.0 = continuous)
is_discrete	True when dt > 0
order	Degree of the denominator polynomial
evaluate(s)	Evaluate $G(s)$ or $H(z)$ at a complex point
feedback(sensor=None)	Closed-loop $T = G\,/\,(1 + G H)$ with negative feedback
to_state_space()	Converts to StateSpace (controllable canonical form)
c2d(dt, method='zoh')	Discretises (equivalent to c2d() API call)
evolve(x, u)	One discrete step — requires is_discrete == True
simulate(t, u)	Batch simulation for arbitrary input
__mul__, __add__, __truediv__, __neg__	Block algebra operators

nota

feedback(sensor=None) uses only the sensor parameter — there is no sign argument. Positive feedback must be implemented manually.

StateSpace

Represents an LTI system via $(A, B, C, D)$ matrices.

Attribute / Method	Description
A, B, C, D	System matrices (NumPy arrays)
dt	Sample time (0.0 = continuous)
n_states, n_inputs, n_outputs	System dimensions
is_discrete	True when dt > 0
evaluate(s_or_z)	Evaluate the transfer matrix at a complex point
bode(omega=None)	Returns (omega [rad/s], mag [dB], phase [deg])
evolve(x, u)	One discrete step — raises RuntimeError if continuous
simulate(t, u)	Batch simulation for arbitrary input
to_transfer_function()	Converts to TransferFunction
c2d(dt, method='zoh')	Discretises the continuous system
__mul__, __add__, __neg__	Block algebra operators

atenção

evolve(x, u) requires a discrete system. Call c2d() first if your model is continuous. It does not mutate internal state — callers must store x_next between steps.

Source

See synapsys/core/ on GitHub.