synapsys 0.2.0

Modern Python control systems framework with distributed multi-agent simulation

Synapsys

Modern Python control systems framework with distributed multi-agent simulation

📖 Documentation · 🚀 Quickstart · 📦 PyPI · 💡 Examples · 📋 Changelog

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Overview

Synapsys is an open-source Python library for modelling, simulating, and deploying control systems. It provides a MATLAB-compatible API built on SciPy, a modern multi-agent simulation framework, and a pluggable transport layer (shared memory / ZeroMQ) that scales from a single laptop to distributed lab setups.

from synapsys.api import tf, ss, feedback, step, c2d

# SISO
G    = tf([1], [1, 2, 1])    # G(s) = 1 / (s² + 2s + 1)
T    = feedback(G)            # closed-loop: T = G / (1 + G)
t, y = step(T)                # step response
Gd   = c2d(G, dt=0.02)       # ZOH discretisation at 50 Hz

# MIMO
G_mimo = tf([[[ 1], [0]],     # 2×2 transfer-function matrix
             [[ 0], [1]]],
            [[[1,1],[1]],
             [[1],[1,2]]])
T_mimo = feedback(G_mimo)     # state-space closed-loop

pip install synapsys

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See it in action

Neural-LQR controller on a 2-DOF mass-spring-damper — MLP initialized with LQR optimal gains tracking setpoint x₂ = 1 m:

Full walkthrough: SIL + Neural-LQR example →

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Features

Feature	Description
⚡ MATLAB-Compatible API	tf(), ss(), c2d(), step(), bode(), feedback(), lsim() — same names, pure Python
📐 LTI Core	TransferFunction, StateSpace, and TransferFunctionMatrix with operator overloading, poles, zeros, stability
🔀 MIMO Support	TransferFunctionMatrix for multi-input multi-output plants · MIMO feedback() · transmission zeros via Rosenbrock pencil
🧮 Control Algorithms	Discrete PID with anti-windup · LQR via algebraic Riccati equation (Q/R validated)
🤖 Multi-Agent Simulation	PlantAgent and ControllerAgent with lock-step and wall-clock sync
🔗 Pluggable Transport	Zero-copy shared memory (single-host) · ZeroMQ PUB/SUB and REQ/REP (distributed)
🔌 Hardware Abstraction	HardwareInterface contract enables seamless MIL → SIL → HIL transitions
🧱 Matrix Builders	StateEquations, mat(), col(), row() — define state-space models from named equations

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Installation

pip install synapsys

Requirements: Python ≥ 3.10 · NumPy ≥ 1.24 · SciPy ≥ 1.10 · pyzmq ≥ 25.0

For development:

git clone https://github.com/synapsys-lab/synapsys.git
cd synapsys
uv sync --extra dev

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Quickstart

1 · LTI systems and frequency analysis

from synapsys.api import tf, ss, bode, feedback, c2d

# SISO second-order transfer function
G = tf([1], [1, 2, 1])

# Closed-loop with unity negative feedback
T = feedback(G)

# Frequency response
w, mag, phase = bode(G)

# ZOH discretisation at 50 Hz
Gd = c2d(G, dt=0.02)

# MIMO: 2×2 transfer-function matrix
G_mimo = tf(
    [[[1], [0]], [[0], [1]]],           # numerators
    [[[1, 1], [1]], [[1], [1, 2]]],     # denominators
)
T_mimo = feedback(G_mimo)               # returns StateSpace

2 · Control algorithms

from synapsys.algorithms import PID, lqr
import numpy as np

# Discrete PID with anti-windup saturation
pid = PID(Kp=3.0, Ki=0.5, Kd=0.1, dt=0.01, u_min=-10.0, u_max=10.0)
u   = pid.compute(setpoint=5.0, measurement=y)

# LQR — solves the algebraic Riccati equation
A = np.array([[0., 1.], [-2., -3.]])
B = np.array([[0.], [1.]])
K, P = lqr(A, B, Q=np.eye(2), R=np.eye(1))
# Control law: u = −K · x

3 · State-space from named equations

from synapsys.utils import StateEquations

m, c, k = 1.0, 0.1, 2.0

eqs = (
    StateEquations(states=["x1", "x2", "v1", "v2"], inputs=["F"])
    .eq("x1", v1=1).eq("x2", v2=1)
    .eq("v1", x1=-2*k/m, x2=k/m,  v1=-c/m)
    .eq("v2", x1=k/m,  x2=-2*k/m, v2=-c/m, F=1/m)
)

print(eqs.A)   # 4×4 system matrix
print(eqs.B)   # 4×1 input matrix

4 · Multi-agent closed-loop simulation

from synapsys.api import ss, c2d
from synapsys.agents import PlantAgent, ControllerAgent, SyncEngine, SyncMode
from synapsys.algorithms import PID
from synapsys.transport import SharedMemoryTransport
import numpy as np

# Discretise G(s) = 1/(s+1) at 100 Hz
plant_d = c2d(ss([[-1]], [[1]], [[1]], [[0]]), dt=0.01)

with SharedMemoryTransport("demo", {"y": 1, "u": 1}, create=True) as bus:
    bus.write("y", np.zeros(1))
    bus.write("u", np.zeros(1))

    pid  = PID(Kp=4.0, Ki=1.0, dt=0.01)
    law  = lambda y: np.array([pid.compute(setpoint=3.0, measurement=y[0])])
    sync = SyncEngine(SyncMode.WALL_CLOCK, dt=0.01)

    PlantAgent("plant", plant_d, bus, sync).start(blocking=False)
    ControllerAgent("ctrl",  law,  bus, sync).start(blocking=True)

5 · MIL → SIL → HIL — swap transport, keep algorithm

# MIL — shared memory, single host
from synapsys.transport import SharedMemoryTransport
bus = SharedMemoryTransport("demo", {"y": 1, "u": 1}, create=True)

# SIL — ZeroMQ, cross-process / cross-machine
from synapsys.transport import ZMQTransport
bus = ZMQTransport("tcp://localhost:5555", mode="pub")

# HIL — real hardware
from synapsys.agents import HardwareAgent
from synapsys.hw import MockHardwareInterface   # replace with your driver
hw    = MockHardwareInterface(n_inputs=1, n_outputs=1, plant_fn=my_hw)
agent = HardwareAgent("hw", hw, bus, sync)

# PlantAgent / ControllerAgent stay exactly the same in all three modes

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Examples

Example	Description
basic/step_response.py	Step response of a 2nd-order system
distributed/plant.py + controller.py	Two-process PID loop via shared memory
distributed/plant_zmq.py	Same loop over ZeroMQ (cross-machine)
advanced/01_custom_signals.py	Custom reference signals with lsim()
advanced/02a_sil_plant.py + 02b_sil_ai_controller.py	SIL + Neural-LQR PyTorch controller
advanced/03_realtime_scope.py	Text-mode real-time oscilloscope
advanced/04_realtime_matplotlib.py	Live matplotlib oscilloscope
advanced/05_digital_twin.py	Digital twin with mechanical wear detection
quickstart_en.ipynb	Interactive Jupyter notebook walkthrough

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Architecture

synapsys/
├── api/            # MATLAB-compatible façade  (tf, ss, c2d, step, bode, feedback, …)
├── core/           # LTI math — TransferFunction, StateSpace, TransferFunctionMatrix, LTIModel
├── algorithms/     # PID (discrete, anti-windup), LQR (ARE solver)
├── agents/         # PlantAgent, ControllerAgent, HardwareAgent, SyncEngine
├── transport/      # SharedMemoryTransport, ZMQTransport, ZMQReqRepTransport
├── hw/             # HardwareInterface (abstract) + MockHardwareInterface
└── utils/          # StateEquations, mat(), col(), row()

The transport layer is the key abstraction: agents communicate exclusively through a TransportStrategy interface. Swapping the concrete transport (shared memory ↔ ZMQ ↔ real hardware) requires changing one line — algorithms and agents are untouched.

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Testing

uv run pytest                      # run all tests
uv run pytest --cov=synapsys       # with coverage report
uv run mypy synapsys               # type checking
uv run ruff check synapsys tests   # linting

Metric	Value
Test suite	184 tests
Coverage	90 %
Type checking	mypy strict — 0 errors
Python versions	3.10 · 3.11 · 3.12

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Roadmap

Version	Status	Features
v0.1.0	✅ Released	SISO LTI, PID, LQR, multi-agent, shared memory, ZMQ, hardware abstraction, Neural-LQR example
v0.2.0	✅ Released	MIMO support — TransferFunctionMatrix, MIMO feedback(), transmission zeros (Rosenbrock pencil), LQR Q/R validation, covariant LTI type annotations
v0.3	🔜 Planned	margin(), rlocus(), pole_placement() · State estimation — Kalman filter, Luenberger observer
v0.5	🔜 Planned	Real hardware drivers (serial, CAN, FPGA via PYNQ)

See CHANGELOG.md for the full release history.

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Citing

If you use Synapsys in academic work, please cite it as:

BibTeX

@software{synapsys2026,
  author    = {Farias, Oseias D. and contributors},
  title     = {Synapsys: A Python Framework for Modelling and
               Real-Time Simulation of Linear Control Systems},
  year      = {2026},
  url       = {https://github.com/synapsys-lab/synapsys},
  license   = {MIT},
}

APA

Farias, O. D., & contributors. (2026). Synapsys: A Python framework for
modelling and real-time simulation of linear control systems.
https://github.com/synapsys-lab/synapsys

For the version number, use the version shown on PyPI.

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Contributing

Contributions are welcome! Please open an issue to discuss what you'd like to change before submitting a pull request.

git clone https://github.com/synapsys-lab/synapsys.git
cd synapsys
uv sync --extra dev
uv run pytest          # make sure everything passes before you start

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License

MIT © 2026 Synapsys Contributors