Welcome to the Synapsys Blog
Welcome to the Synapsys Blog — a space dedicated to practical control systems engineering, academic research, and real-world applications of the Synapsys library.
What you'll find here
This blog is aimed at researchers, graduate students, and engineers who work at the intersection of classical control theory and modern software. Each post will focus on a concrete problem and show how to solve it end-to-end using Synapsys.
Series planned
| Series | What it covers |
|---|---|
| Control Theory in Practice | Modelling, analysis and design from first principles |
| From Simulation to Hardware | MIL → SIL → HIL step by step |
| AI-Augmented Control | Neural-LQR, RL policies and PyTorch integration |
| Research Snippets | Short posts connecting library features to published papers |
| Release Notes | What's new in each version with worked examples |
Quick taste: step response in 5 lines
from synapsys.api import tf, feedback, step
G = tf([1], [1, 2, 1]) # G(s) = 1 / (s² + 2s + 1)
T = feedback(G) # unity negative feedback
t, y = step(T) # simulate step response
The closed-loop DC gain converges to 1.0, and the natural frequency rad/s with (critically damped) — as expected from the denominator.
pip install synapsys # or: uv add synapsys
Why Synapsys?
Most Python control libraries focus on analysis. Synapsys adds a simulation and deployment layer on top: agents that run in real time, a transport-agnostic communication bus, and a hardware abstraction that makes MIL/SIL/HIL a configuration change rather than a rewrite.
It was built alongside graduate research in multi-agent control systems and is designed to be readable, testable, and academically citable.
Stay connected
The first technical post — Stabilising an Inverted Pendulum with LQR — is coming up next.